{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a93186b6-78f0-467e-a1db-b8f3ab406b28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T05:56:51.606124Z",
     "start_time": "2024-11-22T05:56:51.430238Z"
    }
   },
   "outputs": [],
   "source": [
    "import pymysql\n",
    "from pymysql import Connection\n",
    "from pymysql.cursors import DictCursor\n",
    "def get_connection(autocommit: bool = True) -> Connection:\n",
    "    db_conf = {\n",
    "            \"host\": \"192.168.98.55\",\n",
    "            \"port\": 4000,\n",
    "            \"user\": \"dataware_house_testUser\",\n",
    "            \"password\": \"IlGiUL2qcdqckoIzj6c4\",\n",
    "            \"database\": \"dataware_house_test\",\n",
    "            \"autocommit\": autocommit,\n",
    "            # \"cursorclass\": DictCursor,\n",
    "        }\n",
    "    conn:Connection = pymysql.connect(**db_conf)\n",
    "    return conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d81f768-995d-4dc1-b501-d86432e1307e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T05:57:02.227735Z",
     "start_time": "2024-11-22T05:56:55.907605Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_19992\\1192151514.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql_query, conn)\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                author_id         author    author_abbr\n0       00854JP1MPDO2J1WMFBG2JP16JC04_1_5       Altes, T       Altes, T\n1       00854JP1MPDO2J1WMFBG2JP16JC04_1_6       Berr, SS       Berr, SS\n2       00854JP1MPDO2J1WMFBG2JP16JC05_1_1   Bremerich, J   Bremerich, J\n3       00854JP1MPDO2J1WMFBG2JP16JC05_1_2       Saeed, M       Saeed, M\n4       00854JP1MPDO2J1WMFBG2JP16JC05_1_3    Roberts, TP    Roberts, TP\n...                                   ...            ...            ...\n999995  00855HL167BG3HGM6HBNDJP1MPCG7_1_2      Moreno, J      Moreno, J\n999996  00855HL167BG3HGM6HBNDJP1MPCG7_1_3      Cortes, J      Cortes, J\n999997  00855HL167BG3HGM6HBNDJP1MPCG8_1_1  Jonquieres, I  Jonquieres, I\n999998  00855HL167BG3HGM6HBNDJP1MPCG8_1_2     Marenco, A     Marenco, A\n999999  00855HL167BG3HGM6HBNDJP1MPCG8_1_3      Rosset, R      Rosset, R\n\n[1000000 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>author_id</th>\n      <th>author</th>\n      <th>author_abbr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC04_1_5</td>\n      <td>Altes, T</td>\n      <td>Altes, T</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC04_1_6</td>\n      <td>Berr, SS</td>\n      <td>Berr, SS</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_1</td>\n      <td>Bremerich, J</td>\n      <td>Bremerich, J</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_2</td>\n      <td>Saeed, M</td>\n      <td>Saeed, M</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_3</td>\n      <td>Roberts, TP</td>\n      <td>Roberts, TP</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>999995</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG7_1_2</td>\n      <td>Moreno, J</td>\n      <td>Moreno, J</td>\n    </tr>\n    <tr>\n      <th>999996</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG7_1_3</td>\n      <td>Cortes, J</td>\n      <td>Cortes, J</td>\n    </tr>\n    <tr>\n      <th>999997</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_1</td>\n      <td>Jonquieres, I</td>\n      <td>Jonquieres, I</td>\n    </tr>\n    <tr>\n      <th>999998</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_2</td>\n      <td>Marenco, A</td>\n      <td>Marenco, A</td>\n    </tr>\n    <tr>\n      <th>999999</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_3</td>\n      <td>Rosset, R</td>\n      <td>Rosset, R</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000000 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "with get_connection() as conn:\n",
    "    sql_query = \"SELECT author_id,author,author_abbr FROM `dataware_house_test`.`base_dim_author` WHERE `author` <> '' AND `author_abbr` <> ''  limit 1000000\"\n",
    "    df = pd.read_sql(sql_query, conn)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56b9d38b-e022-4e25-8310-0c64e0bfd6f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T06:07:20.622827Z",
     "start_time": "2024-11-22T06:07:20.605776Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8708333333333332\n"
     ]
    }
   ],
   "source": [
    "%run authorsmi.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa91de77-e1a0-46a9-aa8b-251e89750317",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T06:07:55.966840Z",
     "start_time": "2024-11-22T06:07:24.644244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                author_id         author         author_abbr  \\\n0       00854JP1MPDO2J1WMFBG2JP16JC04_1_5       Altes, T       Ebrahimian, H   \n1       00854JP1MPDO2J1WMFBG2JP16JC04_1_6       Berr, SS             Kay, RB   \n2       00854JP1MPDO2J1WMFBG2JP16JC05_1_1   Bremerich, J            Chan, SW   \n3       00854JP1MPDO2J1WMFBG2JP16JC05_1_2       Saeed, M  Richards-Kortum, R   \n4       00854JP1MPDO2J1WMFBG2JP16JC05_1_3    Roberts, TP      Weidenmann, KA   \n...                                   ...            ...                 ...   \n999995  00855HL167BG3HGM6HBNDJP1MPCG7_1_2      Moreno, J            Finzi, L   \n999996  00855HL167BG3HGM6HBNDJP1MPCG7_1_3      Cortes, J          Cerrito, L   \n999997  00855HL167BG3HGM6HBNDJP1MPCG8_1_1  Jonquieres, I            Aslan, G   \n999998  00855HL167BG3HGM6HBNDJP1MPCG8_1_2     Marenco, A           Biswas, S   \n999999  00855HL167BG3HGM6HBNDJP1MPCG8_1_3      Rosset, R              Ma, XY   \n\n        jaro_similarity  jaro_normalized_similarity  \n0              0.440847                    0.467949  \n1              0.486310                    0.511905  \n2              0.412722                    0.472222  \n3              0.376710                    0.453704  \n4              0.470887                    0.495671  \n...                 ...                         ...  \n999995         0.540972                    0.569444  \n999996         0.665000                    0.700000  \n999997         0.504256                    0.535256  \n999998         0.517222                    0.544444  \n999999         0.000000                    0.000000  \n\n[1000000 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>author_id</th>\n      <th>author</th>\n      <th>author_abbr</th>\n      <th>jaro_similarity</th>\n      <th>jaro_normalized_similarity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC04_1_5</td>\n      <td>Altes, T</td>\n      <td>Ebrahimian, H</td>\n      <td>0.440847</td>\n      <td>0.467949</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC04_1_6</td>\n      <td>Berr, SS</td>\n      <td>Kay, RB</td>\n      <td>0.486310</td>\n      <td>0.511905</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_1</td>\n      <td>Bremerich, J</td>\n      <td>Chan, SW</td>\n      <td>0.412722</td>\n      <td>0.472222</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_2</td>\n      <td>Saeed, M</td>\n      <td>Richards-Kortum, R</td>\n      <td>0.376710</td>\n      <td>0.453704</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>00854JP1MPDO2J1WMFBG2JP16JC05_1_3</td>\n      <td>Roberts, TP</td>\n      <td>Weidenmann, KA</td>\n      <td>0.470887</td>\n      <td>0.495671</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>999995</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG7_1_2</td>\n      <td>Moreno, J</td>\n      <td>Finzi, L</td>\n      <td>0.540972</td>\n      <td>0.569444</td>\n    </tr>\n    <tr>\n      <th>999996</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG7_1_3</td>\n      <td>Cortes, J</td>\n      <td>Cerrito, L</td>\n      <td>0.665000</td>\n      <td>0.700000</td>\n    </tr>\n    <tr>\n      <th>999997</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_1</td>\n      <td>Jonquieres, I</td>\n      <td>Aslan, G</td>\n      <td>0.504256</td>\n      <td>0.535256</td>\n    </tr>\n    <tr>\n      <th>999998</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_2</td>\n      <td>Marenco, A</td>\n      <td>Biswas, S</td>\n      <td>0.517222</td>\n      <td>0.544444</td>\n    </tr>\n    <tr>\n      <th>999999</th>\n      <td>00855HL167BG3HGM6HBNDJP1MPCG8_1_3</td>\n      <td>Rosset, R</td>\n      <td>Ma, XY</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000000 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rapidfuzz.distance import Jaro\n",
    "from rapidfuzz import fuzz\n",
    "\n",
    "# 对 'col_name' 列进行打乱\n",
    "df['author_abbr'] = df['author_abbr'].sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# 使用 Jaro.similarity 计算相似度并创建新列\n",
    "df['jaro_similarity'] = df.apply(lambda row: AuthorRatio(row['author'], row['author_abbr']), axis=1)\n",
    "\n",
    "# 使用 Jaro.normalized_similarity 计算标准化相似度并创建新列\n",
    "df['jaro_normalized_similarity'] = df.apply(lambda row: Jaro.normalized_similarity(row['author'], row['author_abbr']), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3c346ee1018bc92",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-11-22T06:08:00.791385Z",
     "start_time": "2024-11-22T06:08:00.593757Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                author_id             author author_abbr  \\\n",
      "577467  00855JP1MPD82J9V67D85JP1MLBO3_1_3      Zhao, Yunfeng     Zhao, Y   \n",
      "607555  00855JP1MPD82J9W6DCO7JP1MLD00_1_5          Zhang, Z.    Zhang, Z   \n",
      "177359  00854JP1MPD05JP067D01JP1MPDG4_1_1           Wang, T.     Wang, T   \n",
      "634115  00855JP1MNDO1JVXM9DO7JP06FD88_1_9           Wang, K.     Wang, K   \n",
      "315299  00854JP1MPCO5ILV6DD84JP1MHBO4_1_2           Zhou, Y.     Zhou, Y   \n",
      "...                                   ...                ...         ...   \n",
      "480954  00854JP1MPDG1JLW6LDG2JP1MPDO5_1_1         Noroski, L       Li, S   \n",
      "480958  00854JP1MPDG1JLW6LDG3JP1MPD08_1_1        Stamaty, MA      Li, YK   \n",
      "480985  00854JP1MPDG1JLW6LDG4JP1MPD88_1_4  Hajja-Hassouni, N  Wright, RJ   \n",
      "480989  00854JP1MPDG1JLW6LDG4JP1MPDG0_1_1    Pego-Reigosa, R     Luna, L   \n",
      "999999  00855HL167BG3HGM6HBNDJP1MPCG8_1_3          Rosset, R      Ma, XY   \n",
      "\n",
      "        jaro_similarity  jaro_normalized_similarity  \n",
      "577467              1.0                    0.846154  \n",
      "607555              1.0                    0.962963  \n",
      "177359              1.0                    0.958333  \n",
      "634115              1.0                    0.958333  \n",
      "315299              1.0                    0.958333  \n",
      "...                 ...                         ...  \n",
      "480954              0.0                    0.000000  \n",
      "480958              0.0                    0.000000  \n",
      "480985              0.0                    0.000000  \n",
      "480989              0.0                    0.000000  \n",
      "999999              0.0                    0.000000  \n",
      "\n",
      "[1000000 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "df_sorted = df.sort_values(by='jaro_similarity', ascending=False)\n",
    "print(df_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "filtered_df = df_sorted[df_sorted['jaro_similarity'] > 0.3]\n",
    "filtered_df.to_excel('output.xlsx', index=False) "
   ],
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T06:10:31.492396Z",
     "start_time": "2024-11-22T06:09:18.367980Z"
    }
   },
   "id": "d7af0c27-b1f6-4954-a806-55ae1cf14c89",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9beeb18c-149c-4763-bb51-0e624e9a8e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 1.0, 0.0, 0.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from rapidfuzz.distance import Jaro\n",
    "a  = Jaro.distance(\"Tran, V\", \"Vu Tran\")\n",
    "b = Jaro.normalized_distance(\"Tran, V\", \"Vu Tran\")\n",
    "c = Jaro.similarity(\"Tran, V\", \"Vu Tran\")\n",
    "d = Jaro.normalized_similarity(\"Tran, V\", \"Vu Tran\")\n",
    "a,b,c,d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a5c35d5a-0ffc-4298-a212-0e1f54a1202e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.72727272727273"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rapidfuzz import fuzz\n",
    "fuzz.partial_ratio(\"Tran, V\", \"Vu Tran\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dab3aadb-7e06-453d-af6b-6e46013aca3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57.14285714285714, 81.42857142857143)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = fuzz.QRatio(\"Tran, V\", \"Vu Tran\")\n",
    "b = fuzz.WRatio(\"Tran, V\", \"Vu Tran\")\n",
    "a,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f8ee1e0-cf88-471b-80f6-d7e601441583",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-22T05:58:04.138119Z",
     "start_time": "2024-11-22T05:58:04.135362Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe4c16a8-1e93-4a26-b23b-691b39e7675f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bNkz333+/2traNHPmTPXt21f5+fk67bTTVFlZucfnNnfuXB1yyCEqKChQ//79ddppp2nt2rUdbvPuu+9qv/32o4sJoBOCIgBIWr16tQYMGKC6urroZUuXLtUhhxwS/fy6665TeXm56urq5HRGXj7feusteb1eHXzwwVqwYEH0tv/85z/Vv39/SdJ//vMfXXDBBaqvr9eqVatUUlKi5557LnrboUOHatGiRTr44IM1ZMiQDnWdeuqpWrBgga666ir985//1CuvvKL33ntPmzZt0qmnnrrb51ReXq6TTjpJxxxzjBYvXqy5c+equrq6w9eVl5frxBNP1NFHH63FixfTxQTQAUERQNo7+OCDdcopp6itrU2GYUQvP/nkkzVmzJjo55MmTVJFRYUkKTs7W5KiwbKoqCh6nSQtW7ZMFRUVGjBggMrKyuRwOCRJra2tOuOMM/SHP/whetsBAwaof//+2rp1q0pKSqKXDxs2TFOmTFFDQ4Oeeuop3XfffTrmmGO0//7765lnntH8+fP10Ucf7fJ5ffrppwqFQrrjjjs0YsQI7bfffrr66qu1ePFiBQIBSdLjjz+uYcOG6be//a3GjRunyy+/XDNmzND999/f028nABshKAJIexMmTNC8efP02muvRQOdJK1fv1577bXXHr8+Oztbr7zyiubMmRNdkLK9Y1hQUKB3331XM2fOjN7+kUce0fLly/d4v+eee260nmAwqKlTp0avGzt2rIYMGdKhi7mz/fffX06nU88884xCoZDq6+v1pz/9SVOnTlVmZqYkacGCBR3uV5KmTZu22/sFkD4IigDS3qeffqotW7Zo+vTpHTqKH330kR577LHo559//rmKi4slKbpwxefz6a233tKQIUM0adIk3XLLLXrmmWeiQWzt2rUqKyvT7373u+j9VFdXa5999lFWVpYkqaqqSpJUXFwc/X9Juvjii1VZWamcnBy53W75fL4Ode/cxdzZsGHDNG/ePP3qV79SVlaWfD6fNmzYoDlz5kRvU1FRoaKiok7329DQoNbW1j1/8wDYGkERQNo75phjdOaZZ6p///6aMmVK9PK+fft2mKs3e/ZsDRs2TD6fT+FwWJJ07LHHauLEiaqsrNS1116rW2+9Veedd54eeeQRSdLDDz8sn8+nww8/XHl5edH7Ov/881VWViZJWrdunSRp8uTJ0YUzLpdLAwcO1JtvvqlRo0bt8TmMHz9e+fn5ys/P1wknnCApEgIvvvhinXvuuVq0aJHee+89ud1uzZgxo0MgBoBdyTC7AAAw26hRo9TQ0KBZs2Zp8+bN0csHDx6s5cuXy+FwyDAMvffeezrqqKPU2NgoSXI4HLrkkkv005/+VAMHDtTJJ58c/doLLrhAV199tWbOnKn99ttPRUVFam5ujl5/7rnnqrS0VD/60Y8UCoVUUlKigoIChUIhSdJxxx2nRx99VHPmzNGdd96p66+/XnV1dR26ipWVldEO5+uvvx6dd5iTkyMpMsTt9Xo1e/bs6Nc8//zzGjx4sBYuXKhDDjlExcXFnVZPV1ZWyuPxRO8HQBozACDNSdrlh8fjMR544AFjzJgxHS53Op2G1+s1srKyjGOPPdZYuXJlp/vde++9Y95nbm5u9Da33HKL4XA4Ot3G7XYbw4cPN5555hmjrq7OyMzMNP7yl79Ev27FihWGJGPBggW7fF6/+MUvjIMOOqjDZZs2bTIkGR9++KFhGIZx7bXXGhMmTOhwm7POOsuYNm1aj76XAOzFYRiMPwBAKgiHwxo2bJjOOuss3X333R2uu+yyy/T666/r2Weflcfj0RVXXCFJmj9//i7v7+2339bUqVN166236qyzzlJjY6N+9atfacWKFVq+fLlycnJUXl6uCRMmaObMmbrgggv09ttva9asWfr3v/+tadOmJfT5Akh9BEUASBHz5s3TtGnTtHLlSo0ePbrDdW1tbfrlL3+pl156Se3t7Zo2bZoeffTR6NDzrvz5z3/W7NmztWrVKuXm5mry5Mm65557NHbs2Oht3n33XV111VVatmyZBg0apJtuuknnnXdeIp4iAIshKAIAACAmVj0DAAAgJoIiAAAAYiIoAgAAICaCIgAAAGIiKAIAACAmgiIAAABiIigCAAAgJoIiAAAAYiIoAgAAICaCIgAAAGIiKAIAACAmgiIAAABiIigCAAAgJoIiAAAAYiIoAgAAICaCIgAAAGIiKAIAACCmDLMLAIB4CYbD8odDCoRD8odD8oe++zxkGDIMQ4YkQ4YMI/Knp6lZNYZPDklOh0P5mU55nE45HQ45nA45d/hwuZzKzHQpI9Mph8Nh9tMFgIQjKAJIScFwWM1Bv5qDATUF/GoK+tUcCEQv84e2hcFtH4FQWGEZ3X6cyQ11mlc9KPr5uH556t8c3OPXZWQ4lZHpVGaGSxmZLmVmOr/7MyPyZ6bbpaysDGVlZ8rtdnW7NgAwG0ERgClaggHVtrdqa3ub6gNtag4E1BT0qykQCYJtoT2HtUToap8wGAwrGAyrTV2r0+l0bAuNGcrOzlB2TqayczKVk5Op7JwMZWVl0KUEkHIIigASJhgOq87fpq3traptb1OtPxIMa/2tag+FzC4vJkeXo2L3hMOGWlsDam0NxH5ch0PZORnKy3MrL9+tvLws5eW7lZvnltNJgARgDoIigLioa29TZVuTKlubtaWtRbXtbWoMtPdgMDi5dq7PrExmGIZaWwJqbQmoektz9HKHQ8rN3RYe87O2/elWTk4mHUgACUdQBNBtDf52VbRGQmFla7Mq25pStkO4Z47dfGY+w5Cam/1qbvZLlU3Ry51Oh/Ly3Mr3ZMnjzZbHm628PDfhEUBcERQB7FZLMKBNLY3bQmEkHLaaNH8wMTr2FK0Ss8JhQ42N7WpsbNfmjQ2SJFeGUwWeLHm92RpSJmVkeCS5Ta0TgLURFAF00BYKakNzg75tatD65nrVtLeaXVJSWSUoxhIKhlW3tVUNdW0qG75SkRCcL6nPDh+5ZpYIwGIIikCa84dC2tDSoPVNDVrf3KAtbc0pP68wkawcFLcr8LjkdG7/W2za9vHtts9zJfWX1G/bR2byCwRgGQRFIM0Ew2FtbGnU+qZ6rW9uUGVrc4/2H7SLnZ+5Heb4eby7mxrQImndtg+HJJ8iwbH/tv+3/vMHED8ERSANtAaDKm+s1deNtVrXVKdAOGx2SSnLaVg/NHt9zXu+kaRITK7d9rFKke5iX0VC4wBJOQmpD4B1EBQBm6r3t2lNQyQcbmpuTOuu4e7Zr4Pm9VX38CsDkiq2fUiSV1KxpBJF5joCSDcERcBGKlubtaZhq75urFV1W4vZ5VjDTvnZ6rExO8clt7stTvdWv+1jpSJBsUSR4OiN0/0DSHUERcDiNrU0akVdjdY0blVjwG92OZbTaY6iKVXEj9eXqHtukrR620euvus0+mT97xqAXSEoAhZU52/T8rpqLa+rVp0/Xt0jSNaPPF5vMv49tEj6ZttHtqSBkgZLKkjCYwNIJoIiYBFtoaBW1ddoWV21NrU0ml2ObVk9KHp8dUl+xDZ9Fxq9kgZJKhUbfQP2QFAEUljYMFTeWKdldVv0TWOtQjZYkZtqjJ2joYW/xS6XQ/n5tSZWsH1O43JFVk0P2van08SaAPQGQRFIQVWtzfqydotW1lfb7Li81OewcFD0eF1ypMQTCOu71dNuRTqMg8QiGMB6CIpAigiGw1pZX63FWytV2drVffDQa53GmlMhaPWMxxswu4QY/JLKt314JZUpMqfRZWJNALqKoAiYrM7fpi+2Vuqr2i1qo3uYdJ1XPVt3lqLX12R2CXtQL+kLRYamBysSGtnUG0hlBEXAJGsb6/R5TYXKm+rMLiWtdRqptfA8UI+3xuwSusgvaY0iC2CKFAmM/cwsCMAuEBSBJAqEQ1pWV63Payq0tb3V7HIgydipgWjVfmJuXoYyM9vNLqObDH03l7FAkcBYKt6agNTBTyOQBE0Bvz6r2ayltVVqD4XMLgc76tRRNKWKXvN6rX5+d6OkpYoMSw+VNEyRPRoBmImgCCRQg79dH1dv1Fe1W9jaxiIcFk2KXp9dOtRBRYalyxWZxzhCkZNgAJiBoAgkQG17qz7esknL66oVtmjwSFsW/etK/kbbiRaWtE7St4qskh4pTn4Bko+gCMRRdVuLFm7ZqFX1NVbNG2mn09+TBf/iMjKdys2tM7uMBDEkbdz2UaxIYPSZWRCQVgiKQBxUtjZr4ZYN+rrBzFMx0DM7L1+xXlL0eJ1yWHUVTrdsX/jSX5HA2NfccoA0QFAEemFTS6M+qtqotWxxYxsOC64J8Xr9ZpeQZFu2ffSVNFZSobnlADZGUAR6oLqtRe9XfEtAtCPrNRTl9TWaXYJJaiR9qMiQ9FhJ+eaWA9gQQRHohqaAXx9Wrteyui1WzBPoEmv9zTocksdbbXYZJquQVKnIedKjxWkvQPwQFIEuaA8Ftah6kz6rrlDQsODYJHZp512LHBbbxigvP0MuF0c/RgL+ekUWvZQpMofRbWZBgC0QFIHdCBlhLdlapY+qNqiVc5jTghG21uksXh+/uHQUVuRowG8V2YNxuCSXqRUBVkZQBHZhVX2NPqhcrzp/m9mlIKF2ioWGISstIfZ4m80uIUUFJa2UtFbSOEWOBrTO3yuQKgiKwE42Njfq/Yp12tzaZHYpSDKHJIeFQqIkeX1bzS4hxbVLWqzI5t0TJHlNrQawGoIisE1LMKD3Nq/T8vp0XxiQXowdcqHTaa2Q6HY7lZPDLzRdUyvpv5KGKLJCmvmLQFcQFJH2DMPQktoqfVD5rdpDIbPLQZLtuHTFZbFuosdnrXpTw7eSNksaI2moGI4Gdo+giLRW1dqs/9tUrgqGmSHJ6TS7gu5Jv4224yUg6UtFQuMESX3MLQdIYQRFpCV/KKT5Vev1eU2FxXbNQ/x911FyWqyj6PXVm12CxTVImq/IQpe9JGWZWw6QggiKSDur6mv07uZ1agrSjYE6jD1bKSg6HFKBh4Us8bFRUpWk8Yps2g1gO4Ii0kadv01vb1rLsXvYJSutZSnwZMjpZE5t/AQUWR29UdLe4nQXIIKgCNszDEOfVG/WgqoNnKqCTnacemCljqLXywbwibFF0nuK7L04RCx2QbojKMLW6trb9MbGNdrU0mh2KbAAp8NhmaOePT422k6coKSlinQX95GUZ245gIkIirCtL2oq9H7ltwqE6SKia5wOWSYostF2MmxVpLs4RpGjAOkuIv0QFGE7TQG/5m1co7VNrAhF91hl6Dk726WsrBazy0gTYUnLFdl7cR9JBeaWAyQZQRG2sryuWm9vLmfjbPSIVRazeHxmV5CO6hQ52WUvSWWmVgIkE0ERttAaDOitTeVa1cBwHHou0lFM/bFnr7fN7BLSVFiRjbq3KNJd5BhA2B9BEZb3TWOt3tz4jZqDAbNLgQUZO+RCi4w8s9G26SoVmbs4SVI/k2sBEougCMsKhsN6t2KtlmytMrsU2IQVhp6dTofy8umcm69d0keKLHIZK8li5z8CXURQhCXVtbfpX+tXqaqNCf2IH6dSf+jZ43XJ6UztGtPLN5JqFOku5ptcCxB/BEVYzuqGrZq3YY3awyxYQXxZoaPoYaPtFFSvyEKX8Yps0g3YB0ERlhE2DP234lt9WrPZ7FJgI8YOe+M5LLBPntfXZHYJiCkkaYki3cW9JbnMLQeIE4IiLKEx0K5/r1+tTS28SSJxrNBR9HqrzS4Bu7VRUqOk/cWJLrADgiJS3trGOv1nw9dqDTHkhsRK9VXPObkuZbrbzS4De9Qg6QNF5i0OMLkWoHcIikhZhmFoQdUGLdyyMcWXF8DKjB3CoTPFh569PrMrQNcFJH0sabSkUeL4P1gVQREpqSUY0H/Wf611zewXh+RJ9Y6i19tqdgnotlWKnOoySVKmuaUAPcDGT0g51W0tenHNl4REJF2K50R5fHVml4AeqVJkVXSD2YUA3UZQREopb6zTn7/5Sg0B5mEhSXaY15DKL4gZGQ7l5dWaXQZ6rEXSh5I2mV0I0C0MPSNlfF5ToXc3r2U+IkzjSOGxZ4/XlfJD49iTkKTPJDUpMncRSH0ERZgubBh6Z/NafbG10uxSkOZSOYd5vH6zS0DcrFKkw7i3UruPDRAUYbL2UFD/Wr9a65qYjwhz7NjBTuWgyEbbdrNBUqsi+y26Ta4F2DV+lYFp6v1t+vM3XxESYTJHjP9LPR422rahGknzJTWbXQiwSwRFmGJTS6NeXPOlatrZ7gOpI1VfEPPyM5SRETC7DCREkyKLXFiohNSUqq+LsLHlddV6pXwZJ60g5aRqR9HrDZtdAhLKL2mBWBGNVMQcRSTVZ9Wb9W7FOrPLAKKssMre66Pzbn9hRVZEt0gaaXItwHcIikia+ZXr9dGWjWaXAXS0Q1JM1Y6ix7vV7BKQNCsktUvaS6n7LxLphKCIhDMMQ+9WrNPnNRVmlwLsViq+LWdmOpWbx4ke6aVcUlCR7XNS8V8l0glBEQkVNgzN27hGy+pYsYnUZOzwRpyKk7a9vlSsCom3XpGwOEmp+S8T6YKgiIQJhsN6fcNqfd3Aaj6gpzxejrNMX5sVCYsHSHKZXAvSFb+mICH8oZBeXbeCkAhLScVBPq+v0ewSYKotkhYqEhiB5CMoIu7aQkH9Ze1yfdvMvCqkPiOF1z07HFKBh2kb2KrI9jkc44jkIygirpoDfs35ZpkqWjluDNbjSLHMmF+QIZcrZHYZSAn1ioTFNrMLQZohKCJuGgN+vVy+TNXtLWaXAnRD6h7h5/USErGjRkWO/GNfTSQPQRFx0RTw65XyZarz89suLMxIrZaix8cZwNhZi+gsIpkIiui1ZkIiLGzHaOgwUqun6PWx0TZi2R4WWRGPxCMooldaggH9Ze1y1RISYQOOFFrYkpXlUnY2HUXsSrOkj8QCFyQaQRE91hoM6JXyZappZ74MEG8en9kVIPU1irCIRCMookfaQ0H9de0KQiJsYIfFLCk0R9HLRtvokgZJH0sKmF0IbIqgiG4LhEP6+7qVqmpjWAw2kzo5UR5fvdklwDLqFAmLbMqN+CMooluC4bD+sW6lNrVwWgSQKE6nQwUFNWaXAUuplbRIElsqIb4IiuiysGHoX+tXc+IKbMXYcffEFBl6LvC45HSmRi2wkhpJn0gKm10IbISgiC6bu2GNvmnk7GbYV6qczOL1MYSIntoiaYnZRcBGCIrokg8r12t5PWfOwuZSJCh6vMz/RW9skLTC7CJgEwRF7NHSrVVauGWj2WUAibFDOEyVVc9eH7+Uobe+lrTO7CJgAwRF7FZ5Y53e2lRudhlAUqRCTMzJccntZgN7xMOXkirMLgIWR1DELlW1Nutf61cpnBJvn0Ci7LCYJWz+v3U22kb8GJI+U2RFNNAzBEXE1OBv19/XrVAgzOo52Jux4y9CKTD07PXSTUQ8hRXZNqfJ7EJgUQRFdNIeCurv61aoOchO/0gvjh27iybx+urMLgG241dkQ25O+0H3ERTRQSgc1mvfruJoPqQdp/kZUS6XQ3n5DBMiEVoUCYtsyI3uISiig3kbv9F6NtRGGnI6zE+KHq9LjlTZzBE2VC9psdlFwGIIioiaz16JSEPbT2ZxpkBL0eNlugcSbbMiW+cAXUNQhCTp64at+oi9EpGGtvfvXCnQUfT6WHCAZFghqdLsImARBEWotr1Vb2xYY3YZgKlSoKEoj7fG7BKQNj4XK6HRFQTFNOcPhfTat6vkDzPBGenN7DmKuXkZysxkVSqSJajItjlMd8DuZZhdAMw1d+MaVjjD8m489Di1NX7XHcnOz9PPnnhQQyaOj3n7O793qrZu3Bz93JmRqQtveViTDj5akvT96fuore27n4tLL7lOp8+4UJJ0w68v0cKP31XZ0NF68vFX5XLF52XU62PPUiRbsyKdxQOlFNgaCqmJjmIaW7Rlk1Y3bDW7DKBXbj/+5GhI7Du4VJLU1tSsB8++SCs+WNDp9o9f8vNoSMzMzpIkhYMBPXnTz/TCS4/rrnuujoZEn6dP5GueuEePPXG36uurtfDjd5Wbk6fTTztf/3PFjxQKBePyPLxefmGDGaokrTS7CKQwh2GkwFEESLpvm+r117XLOZwPlvfLiZMlSaMPPVhrFn2mk669Sn+7c7YkKcPt1q/n/V0Ffft0uv3QvSfo6At+rP0KB2nWued0ut+iolLV1dWovb1NLleGHA7J4XAqGAxon70P0oqVS+RwOOVyuTR61HhdeP4vNG7sPtGvf/mVP+jlOU9Kks484+JoR1KSli//Qg/87lY9+rtXoh3Jgw6tUF5efZy/O0BX7SdpoNlFIAXRUUxDDf52/Xv9akIiLK9m83fDx1s3bNQhp03XYWeeIu0w3/Djv/8r5tfu+71jNfHYozR46FA5nC5JUmamW0OHjJQkVVZuVJY70nF0OKTcnHwFAn4VFvZXff1WjR09UQ/e94IefuBl9e9fomtvuEBbqiskSWu+WaFnn3tQv/7V/fr1r+7T088+oG/KI12bUCio+x+6WVf9/LZoSMzIdCo3l5AIM30hqdHsIpCCCIppJhgO65/frlJrnIbLADMte+eD6P/XbNikUYccKElyuiIvbVm5OVr3xZcxvzY/2mV0KCsnV5IUCPg187IbVVDglSQ1NNbL4XDopz+5Qg2NdcrMdCsY9Kt87WotXvKxZl15lmrrturqq+6UYYT1+eeRoe7167/R8GFjtN+kydpv0qEaPmyMvl3/jSTp5TlPae+JB2rsmL2jtXi8TqXA7jxIayFJnyqyyAX4DkExzby1qVyVbc1mlwHEnREOdxhiliRnRoYaa3a95UxrY5PO+t7xamuOdFJyc/M0ad9D9PPLb1FmZqbO+fFMlZaW6Zk/PihJGjJ4uBoa6uR0OFVUVKq29lZddfWPtfDjdxQMBlVQ4JMkDRs2Whs2rlVl1SZVVG7Uho1rNaxslDZu+lZvzPubLjjvyg51eL3++H0jgB5rkhT7FyukL4JiGvmytkpf1W0xuwwgbvY6+vCYl4dDkRXE7pycXX5tU81WZeXl6r6nnpZz2xBwS0uzjv/+eN3/0C267ZZHdP65P9f551whwwgrP9+j6poqSdLw4WPU1BQ56tLlcum2O65Snz79tf9+h0qShg4ZqQvP/4Wuuf58XXvDBbrogl9q6JCRuv/Bm3TJRddo0Scf6IKLT9Qll52kL5YsktfHkB9SxQZJ680uAimEoJgmattb9c7mtWaXAcRV35KS7z5xONRYs1UtLS3StjV62fm5KujbN+bXrl74iZxOp0pKBym8bSpGSfFgjRuzt6b/4Czdc+91Wrvua91+1y/lcmXozNMvUmNjZB5hU3OjBg0qkySFQmEFAu362cXXyr1tTqMkTf/BWXru6bl67um5mv6DszR33t+Uk5OnvfaapP+9/0b95tZHdNnPbtAd/+8qZWVvSsB3B+ipL8V8RWxHUEwDYcPQ6xu+ViDMPm2wH19JUeR/DEN/u/N/ddMhUyVJGVlZathSo6/e/a9+ufeh+vcDj0qSRk0+SJK07L0P9dA5F2vGcUdH72v48DHKdLt18YVXa8TwsbrqF2dLMlQ2dJTK167WIQcdJUmqqNigVau2D9EZys7OVenAIbussb5+q557/mHNmnmzlq/4QoMGlWlQaZkm7XuIQuGg1qz5Nq7fE6B3QpI+2/Yn0h1BMQ3Mr1qvylbmJcKebpr3qrLz8yVJDVuqJcNQZm6Oxhx6kILt2+b+GYa+mPuW/v3Ao7r0iQfVpzTSiVy3+EuFgkE5XRk64sQf6aOF72rqsdMlSU1NzapvrFN2dq5+eOKZ2rhxnX5z6yPyeAq33WWkaznliO/J7XZr8KDhu6zxkcf/n0479Tz171+scCikUPC7BQPhcFChEL/EIdU0SlpmdhFIAQRFm9vQ3KBFWxjWgr3dueBN/XbpAp1ywy9UWFKscCCoxuoaXfz4/frt0gX67dIF8hUXqWFLZGHLjW/8TVMvPk/9hgyS2+2W0+nUN18t1v/87AaNG7uPnvzD/2r110s1+65n9PdXPtITf5itsqEj5XQ69epfFuriC6+Wy+VSXl6Bli3/XDMvvVHNzY1qjfEL2SeffqgNG9bq5Ok/kSSNHbO3vl3/jRZ+/J7+9e8/K8Pl1JgxpUn9fgFds05ShdlFwGRsuG1j7aGgnvt6iRoDrKgEdqWsOqg77rxP67/8WPU1VcrLLdDw4WN05umX6ID9D5MkXXX1T1RcVKrrrrlHknTWOUersnJjp/v66U8u13k/nRX9vL29TRdfdpJuvvF+jRyxV/Tyf/9njp5+9gFlZrr12GOX69RTJyb4WQI9lSlpiqRdLwyDvREUbezf61drZf2utwYBIJVVh7S0wauRfXI1sDW5c7LcbqcOO3J5Uh8T6L6+kg4R50GnJ4aebWpZ7RZCItAF239Tdpiw47XXxxsvrKBGkWFopCOCog3V+9v0NlvhAN1ixouhh422YRnLJbWYXQRMQFC0mbBh6D8bvpY/zLYGQHeYcYSe18f5zrCKkKTF+q4Hj3RBULSZT6o3aVNLk9llANax7X3PmeT5Vw6HVODZmtTHBHpnq6S1ZheBJCMo2khte6sWVG0wuwzAUqJzFJPcKSnwZMjppPMPq1khiX150wlB0SYMw9Cbm8oVYhE70CPJXszi9Qb3fCMg5YQkfSGGoNMHQdEmvqzdog3NDWaXAVhWsqcoenx0ZWBVWyWVm10EkoSgaAPNAb/er2DrAqAnjG0RMdkvhl4f8xNhZSskMR8+HRAUbeDtzWvVzipnoFeS2VHMznYpK4utRmBlYUlLxBC0/REULW5Nw1atbqAzAfRWMqcoenzJeywgcbZKYgGl3REULaw9FNRbm9aaXQZgadtPMXUksTHi9bYl78GAhFouiY3j7YygaGEfVK5XU5AfUCAekjn0zEbbsA+/IvMVYVcERYva1NKoL7ZWml0GYH3bxpyTFRRdLofy8pkuAjv5VlKt2UUgQQiKFhQywnpz4zdmlwHYSrKCYoHHJaeTBQCwmy/FwhZ7Iiha0OKaStW0t5pdBmAryQqKHjbahi3VS2KbNjsiKFpMazCgjzimD7Asr4+952BXKyW1m10E4oygaDHzqzawZyIQR9GznpM0aub1VifngYCkC0haZnYRiDOCooVUt7VoCQtYgLjafjJLMoaec3JdynTTcYGdbZRUY3YRiCOCooW8V7GOqcJAvG37oUpGUPT6kvAggOmWiYUt9kFQtIhvGmu1rom91wAr83o5tg/poF7SJrOLQJwQFC0gbBh6bzOryYBEchiJ74Cw0TbSxwpJzKe3A4KiBSyuqVCtnyO/gERI1gBZRoZDuXlsSox00SpprdlFIA4IiimuNRjUgi1shwMkWqJXPXu8ru2HwABp4mtxDrT1ERRT3IKq9WoP0b4HEi3xQZE3TKSbgCJhEVZGUExhte2tWrK1yuwygLRgJHgQmo22kZ7WSmIRl5URFFPYR1UbFWaLASChovsoJryjyEbbSEdhRU5sgVURFFPU1vZWrajnjQVItER3EiUpLz9DGRmBhD8OkJo2KrJlDqyIoJiiFlRtoJcIJJEjnLifOK83nLD7BqxhudkFoIcIiimopq1Fq+o5AgmwC6+POVpId9WS2B7KigiKKYhuIpBExk5/JoDHxxskIK0yuwD0AEExxVS3tWhVw1azywDSyLbNDRN0MktmplO5uQ0JuW/AWrZIqjO7CHQTQTHFLKhic20gmYxO/xNfXh8vs8B36CpaDa9gKWRLa7NW000EzJGgjqLH256Q+wWsqUqsgLYWgmIKmU83ETCNI0FB0etrTMj9Ata12uwC0A0ExRRR1dqsNY1MeAfME/+DmB0OqcDDfqhARxWSmLdrFQTFFPHRFrqJgFmcjviHREnKL8iQy8VZ7UBndBWtgqCYAura27SmgW4iYAZDDrkS9Ero9RESgdg2S2JahhUQFFPAZzWb2TcRMIlhJK6j6PE2J+R+AXtYY3YB6AKCosnaQkF9VbfF7DKAtJaooOj1sYsBsGubJLWZXQT2gKBosqVbqxQIcw4sYCZnAnJiVpZL2dl0FIFdC0taZ3YR2AOCoonChqHPayrMLgNIe84EJEWPL+53CdjQOknM5U1lBEUTraqvUVPQb3YZQNpzJWDo2ctG20AX+CVtNLsI7AZB0USf1mw2uwQg7RmK7HcYb14fp08AXVNudgHYDYKiSTY2N6iylflLQCqI92IWp9Oh/IKauN4nYF+NktiYPlURFE1CNxFIDYbiHxQLPC45nWx6BXQdXcVURVA0QZ2fDbaBVBLvtSxeXzC+dwjYXqUkRtlSEUHRBJ/XVLDBNpBC4t1R9Hib4np/QHqgq5iKCIpJFgiHtayWDbaBVBLvtSxeH/MTge7bIIlufKohKCbZ6voatYfZMwpIJfHsKObkuOR2c9oE0H1BRc6ARiohKCbZ0toqs0sAsANDjrjOUWSjbaA3vjW7AOyEoJhEW9tbtbGl0ewyAOwknkHR66WbCPRcrSTm+KYSgmISLd1KNxFIRY44zlL0+tjRAOid9WYXgB0QFJMkFA5rWR2LWIBUFK+OosvlUF4+QRHonQ2SwmYXgW0IiknyTWOdWkOs5gJSUbz6iR6vKyHHAQLppV0SI3CpgqCYJF/RTQRSUjxPZvF4A3G5HwAMP6cKgmIStAQDWttYZ3YZAGIwjPh1FL0+JuED8VEliYVhqYCgmATL66oV5iwWIGXFa46ix8tG20B8GIrMVYTZCIpJ8BUnsQApzBGXVc+5eRnKzGyPQz0AIhh+TgUExQTb0tqs6vYWs8sAsEtGXIaevT5WaQLx1Sypwewi0h5BMcFWNWw1uwQAexCPtSxeb2vv7wTATjaZXUDaIygm2Kp65iwBqcxQfF4IPWy0DSQAZz+bjaCYQNVtLar1s2oLSHW9bShmZjqVm1sfl1oA7KhZEj9bZiIoJhDdRCD1GXFYyuLxOtloG0gYhp/NRFBMoNXMTwRSXxz2UfR4/XEpBUAsDD+biaCYIFvbW1XTzuR2wAp6GxS9vsa41AEglhZJdWYXkbYIignCsDNgDYYcvRo2djgkj7c6fgUBiIGuolkIignCsDNgHY5eHJyUl58hlysYv2IAxEBQNAtBMQFq29u0pY1NtgGr6M1yFq8vFMdKAMTG8LNZCIoJsLqBYWfAShy9OIvd6+WXQiA5Ks0uIC0RFBOAYWfAWnoz9Ozx8fMOJEeV2QWkJYJinDUF/KpsbTa7DABd1Jt9FN1up3JymuJaD4BdqZfUbnYRaYegGGfrmthBHrCUXnQTvT522QaSa4vZBaQdgmKcrW2qM7sEAN1gRP/TfWy0DSQbw8/JRlCMI8Mw9C0dRcBijB4PPXt9/LwDybVFvRoGQLcRFOOoqq1FrSH2UwOspiernp1Ohwo8LGQBkisgqdbsItIKQTGO1jHsDFhQzxaz5Be45HSyhyKQfMxTTCaCYhyxkAWwnp7OUfR6GT0AzME8xWQiKMZJIBzSppZGs8sA0BM9CIoeH9tgAeZgm5xkIijGyfrmBoUMJtgCVmOoZ3MUvWy0DZiI4edkISjGCcPOgIV1MydmZ7uUlcXRfYB5OCo3WQiKccJCFsCqHN0+ws/jS0ghALqMjn6yEBTjoDHQrq3tbWaXAaAHDCOyB2p3eL38vAPmahbzFJODoBgHG5pZxAJYWXfnKLLRNpAK6ComA0ExDjaz2hmwLEOSwl3fSdHlcigvnzcowHz8HCYDQTEONrc0mV0CgF7pekexwOOS08kOB4D5CIrJQFDspUA4rC3trH4ELK0bcxS9PjbaBlJDgyR+HhONoNhLla1NCrN/ImBp3Vn17PEyggCkBkN0FROPoNhLDDsD1meEu9FR9FYnsBIA3UNQTDSCYi9tbmUhC2BtDjkcXVvMkpvrUqabLTmA1EFQTDSCYi/RUQSszdn1Bc9stA2knDr16LB2dBlBsRca/O1qDgbMLgNALzi7kRS9XhauAaklLImGTSIRFHuBYWfA+hzqRlBko20gBfFzmUgExV7YxLAzYHnOLr4KZmQ4lJtXm9hiAPRAg9kF2BpBsReYnwhYn7OLC1k8Xpe6eFMASUVHMZEIij1kGIaq25ivBKQLj9dvdgkAYqKjmEgExR6q87craITNLgNAL3W1o+j1MYIApKaAJBo3iUJQ7KEaju0DbKHrQ89stA2kLoafE4Wg2EM1ba1mlwAgDroSFPPyM5SRwVZYQOoiKCYKQbGHqukoArbQlYai18c0EyC1MU8xUQiKPVTTTkcRsIc9J0U22gZSHR3FRCEo9kDYMFRLUARsoSsHs3h87J8IpLZ2RRa1IN4Iij1Q529TyOBsScAO9jRHMTPTqdxchrWA1NdsdgG2RFDsAfZPBOxjT0f4eX28TALWwBZWicArYA8wPxGwjz0tZvF425NTCIBeoqOYCATFHqihowjYxp5eBL2+xqTUAaC3CIqJQFDsATqKgI3spqXocEgFHjbaBqyBoJgIBMVuMgxDdf42s8sAECe7exHML8iQyxVKWi0AeoOgmAgExW5qDgZY8QzYyO7mKHp9hETAOoKSaOTEG0GxmxoDTGwH7GR3a1m8XjoUgLXwMxtvBMVuagz4zS4BQBztbnscj29rEisB0HsExXgjKHZTAx1FwFZ2NfScleVSdjZvOoC18DMbbwTFbqKjCNiLcxcdRa8vuXUAiAfmKMYbQbGbGvx0FIF0wEbbgBURFOONoNhNdBSB9OD11ZtdAoBuIyjGG0Gxm1j1DNhN56Fnp9Oh/IIaE2oB0Du8R8cbQbEbAuGQWkNBs8sAEEexZigWeFxyOtkvFbCe4LYPxAtBsRsYdgbsJ1ZQ9Pp4owGsi+HneCIodgMLWYD04PE2mV0CgB4jKMYTQbEbmoJ0FAG7id1RZH4iYF0ExXgiKHZDG/MTAdvZOSjm5LjkdvNGA1gXo3/xRFDshvZQyOwSAMTZzkf4eXzm1AEgXvhFL54Iit1ARxGwoZ0WN3u9vMkA1kZHMZ4Iit1AUATsZ+cXQa+v1pQ6AMRLwOwCbIWg2A3tBEXAhr4bena5HMrLJygC1sZ7dTwRFLuhjTmKgO04jO/Gnj1elxyxlkEDsBCCYjwRFLuBjiJgPzvmQo+XISvA+vg5jieCYjcwRxGwN6+PjbYB6+O9Op4Iit3A9jiADRnf9RQ9XjbaBqwvqE7bGaDHCIpd5A+FFOYfHmA72+co5uZlKDOTbTUAe6CrGC8ExS5i2Bmwp+0bbnt9YZMrARA/vGfHC0Gxi9rD/KMD7MjY1lH0eltNrgRA/LCgJV4Iil0UDDPsDNjR9qFnNtoG7ITmTrwQFLvIYH4iYEuGHMrMdCont97sUgDEDUExXgiKXRQ2CIqALRmSx+tko23AVnjPjheCYhcRFAF7csiQx+c3uwwAccV7drwQFLuIoAjYVFjyehvNrgJAXPGeHS8ExS5iD0XAnhySPN5qs8sAEFe8Z8cLQbGL6CgC9pSX55TLxcR3wF54z44XgmIXERQBe/J4OZoTsB/es+OFoNhFBEXAnrweNtoGgF3JMLsAq2COYs/MffQpzXvsDx0u6182RNf/82VJ0oJXXtXnr8/ThuUr1d7cojs+nKccT8Fu73PNJ5/r3Wdf0IZlK9WwpVrnPXC3Jh57ZIfbvPPsC3r3mRckSUdf8BMdde7Z0evWLflKf7vjXs168Sm5MvgRSHde31azSwAQdxzJGS+8S3YRHcWeKx45XD978qHo506XK/r/gbY2jTnsEI057BC9/uBjXbo/f2ubBo4epYNO+YGevfKGTtdvWvm15j7ypC58+H9lGIb+cPnVGjP5IJWMHqlQMKi/3H6PfnTL9YRESJKys+koAvbDe3a88E7ZRQTFnnO6XPL06xvzuinnnClJ+nrRZ12+v3FHTNa4Iybv8vqq8rUqGT1Sow4+QJI0cPRIVZWvU8nokXr32Rc0Yv9JGjJhr248A9iZoUw5xDxFAIiFoNhFDnFsQ09Vf7tetx3zQ2W43Rq6zwSdeOVlKiwpTtjjlYweqS1rv1Xt5goZhqEta9ereNQIVa/foI9f/beuevmZhD02rCdsuOTkxxuwGZo78dKtxSyhUEg33XSThg0bppycHI0YMUK33367jB26bYZh6Oabb1ZJSYlycnI0depUrV69eo/3/dZbb+nQQw9VQUGBiouLdd111ykY7LhlxZIlS3TEEUcoOztbgwcP1uzZs7tTfq+4ON+rR4ZMHK8zb/+1Ln7sfp120zXaunGTHjn3MrU1NyfsMYuGl+n7P79Uv7/k53riZ1fq+1deqqLhZfrLbffoB1fN1MoPF+reU36s3/7op1rzyecJqwPWEDT4fRmwH9bqxku3XiHvuecePfbYY/rjH/+o8ePH65NPPtH5558vr9erWbNmSZJmz56thx56SH/84x81bNgw3XTTTZo2bZqWLVum7OzsmPf7xRdf6Pvf/75uvPFGPffcc9q4caMuvfRShUIh/e///q8kqaGhQccff7ymTp2qxx9/XEuXLtUFF1wgn8+nSy65pJffhj1zEhR7ZMch4oFjRmroxPG6Y9op+mLuWzr41OkJe9xDTz9Vh55+avTzRf/4t7LyclW2z0TdPf0MXfnS06qvrNLz196sG9/4qzLc7oTVgtQWDLsk155vB8BK+KGOl24Fxfnz5+ukk07SiSeeKEkqKyvTSy+9pI8//lhSpJv4wAMP6Ne//rVOOukkSdJzzz2noqIivfrqqzrzzDNj3u/LL7+svffeWzfffLMkaeTIkZo9e7ZOP/103XLLLSooKNALL7wgv9+vp59+Wm63W+PHj9fixYt13333JSUouhz8dhIPOZ4C9R86RNXfbkjaYzbV1mneY09r5rOPad3Sr9R/6ODoRygY1Ja136pk9Mik1YPUEgjzsw3YDz/X8dKt7+Shhx6qt956S6tWrZIU6QR+8MEHOuGEEyRJ5eXlqqio0NSpU6Nf4/V6dfDBB2vBggW7vN/29vZO3cacnBy1tbXp008/lSQtWLBAU6ZMkXuHzs+0adO0cuVK1dbWdudp9IiLSUxx0d7Sour1G+Tp3y9pj/na7Ac15Zwz5SseICMcUmiHKQ3hYEjhMNsopDM/QRGS7r77FTkcP9SVVz4Zveyoo26Qw/HDDh+XXvpIl+/z0ksfkcPxQz3wwD+il7W3B3TOOb+Vx3O6Ro/+mf7v/xZ3+Jp77/2brrji971+PuDnOl661VG8/vrr1dDQoLFjx8rlcikUCunOO+/Uj3/8Y0lSRUWFJKmoqKjD1xUVFUWvi2XatGl64IEH9NJLL+n0009XRUWFfvOb30iSNm/eHL3vYcOGdbrf7dcVFhZ256l0G3MUe+a1/31I4488XIUDS1S/ZYvmPvKUnC6XJp1wnCSpobpGjdU10Q7j5tVrlJWXq8KSIuV6vZKkxy66XBOPOVKHn/0jSdvC5g4dya0bN2njilXK9Xo6LZJZOf9jbVn3rc688yZJ0uDxe6mqfJ2W/3eB6ioq5XA5NaBsSMK/D0hdbSHeUNLdokWr9Pvfv6G99y7rdN3FF0/Tb37z4+jnublZXbrPv/99gT76aKUGDuzT4fInnnhDn366RgsW3Kv//OdTnX32/6qy8k9yOBwqL6/Qk0/O1Sef3N+r5wOJoef46VZQnDNnjl544QW9+OKL0aHfK6+8UgMHDtS5557bpfs44YQT9N///leSNHToUH311Vc6/vjjde+99+rSSy/VOeeco6ysLN10003673//K6czNV7EGXrumfrKLXr+ulvUXFev/EKfhu23j2a98KTy+0SC/YI5f++wIfcj510mSTrj9l/roJMjUxxq1m9Uc1199Dbrv1qhxy6YGf38tXsjezQeMP37OmtbIJQiezT+/a7f6px7b4/+O/IVD9ApN/xSL990hzLcbp11503K3MXcWaSH1iC/BKazpqZW/fjHv9WTT16hO+54udP1ublZKi7uXiNi48YaXXHF7zV37m068cTfdLhu+fL1mj79II0fP1TDhxfrmmueUXV1g/r39+qyyx7TPfecJ48nt1fPCRJBMX66FRSvueYaXX/99dG5hhMnTtS6det011136dxzz1VxcaSbU1lZqZKSkujXVVZWat9995UkPfXUU2ptjWxwm5mZGb3NL37xC1111VXavHmzCgsLtXbtWt1www0aPny4JKm4uFiVlZUd6tn++fbHTaRMJ//oeuKce2/f7fXT/uciTfufi3Z7m1/P/XuHz0ceuJ9+u3TXUxm2y8zOjp4As6NDTpuuQ05L3EIaWEsrWyimtZkzH9eJJx6gqVP3jRkUX3jhXT3//DsqLi7UD394kG666Qzl5u76l8twOKxzzrlP11xzqsaPH9rp+n32GaY//ekdtba2a+7cz1RS0kf9+nn0wgvvKjs7U6ecsus9YtEdNHfipVtBsaWlpVOHz+VyRed4DRs2TMXFxXrrrbeiwbChoUELFy7UZZdFOkWlpaW7vH+Hw6GBAwdKkl566SUNHjxY++23nyRp8uTJuvHGGxUIBKIB880339SYMWMSPuwsSZkp0tkEEF8twT3fBvb05z+/r88+W6NFi+6Lef3ZZx+poUMHaODAPlqyZK2uu+5ZrVy5UX/72692eZ/33PNXZWQ4NWvWD2Nef8EFx2nJkrXaa6//Ub9+Hs2Zc61qa5t0880v6N13/59+/es/6c9//q9GjCjW00//XKWlsQ8rwJ7wnh0v3QqKP/zhD3XnnXdqyJAhGj9+vD7//HPdd999uuCCCyRFgt6VV16pO+64Q6NGjYpujzNw4ECdfPLJu73ve++9V9/73vfkdDr1t7/9TXfffbfmzJkj17bj3s4++2zddtttuvDCC3Xdddfpyy+/1IMPPqj770/OXI5Mhp4BW2oKmF0BzLB+/Rb9/OdP6s03f6Ps7NjbY11yyfei/z9xYplKSgp17LG/1po1mzViREmn23/66dd68MHX9NlnD8ixi3ntmZkZeuSRyzpcdv75D2jWrB/q88+/0auvfqQvvnhIs2f/VbNm/V5//euuQyl2h1HAeHEYRtfPpmtsbNRNN92kv//976qqqtLAgQN11lln6eabb46uRjYMQ7fccoueeOIJ1dXV6fDDD9ejjz6q0aNH7/a+jznmGH322Wdqb2/XPvvso1tuuSW6mnq7JUuWaObMmVq0aJH69eunK664Qtddd10Pnnb3tQaDemzFJ0l5LADJMzTPpdOGVe75hrCVV19doFNO+X9yub5rAoRCYTkcDjmdDrW3/y3aqNiuublN+fk/0htv3KZp0/brdJ8PPPAP/eIXf5Bzh10yQqGwnE6nBg/up7Vr/9Dpa955Z4muu+5ZLVhwr6655hllZLg0e/b5+uqrdZoy5QbV1LwYx2edTqZKYv55PHQrKKazYDish5Z9bHYZAOKs0O3U+aOrzC4DSdbY2KJ167Z0uOz88x/Q2LGDdN11MzRhQuf5hR9+uEyHH36dvvjiIe2997BO19fUNGjz5o7btU2bdrPOOedonX/+VI0ZM6jDdW1tfu2335V64YVfatKkEfr5z5+Q0+nQ/fdfrMWLv9FRR/1KdXV/jsOzTUfTJGXu8VbYM86u6qIMp1NOh0NhcjVgKw1+9tFMRwUFuZ3CYF5etvr29WjChKFas2azXnzxPX3/+weob98CLVmyVldd9ZSmTBnfISSOHXup7rrrXJ1yymT17etR376eDveZmZmh4uLCTiFRkm6//c/6/vf316RJIyRJhx02Ttdc84zOP3+qHn74XzrssHEJeObpwCHiTfww8a4bclz8wwPsJiTJMJjPhI7c7gz93/8t1vHH36yxYy/TL3/5B5122qH65z9v7nC7lSs3qr6++2fXf/nlOs2Z84Fuu+27PRpnzDhMJ554gI444notWbJWDz54ca+fR3rKUCQsJl5jY6OuvPJKDR06VDk5OTr00EO1aNGi6PWGYejmm29WSUmJcnJyNHXqVK1evXqP9ztr1iztv//+ysrKii4O3tmSJUt0xBFHKDs7W4MHD9bs2bM73eaVV17R2LFjlZ2drYkTJ+r111/v9nNk6Lkbnvt6iarbWswuA0CcXTm+SU5Hm9llAIiLPElHJ+WRzjjjDH355Zd67LHHNHDgQD3//PO6//77tWzZMpWWluqee+7RXXfdpT/+8Y/RBb5Lly7VsmXLOp1It6NZs2ZpzJgxWrhwoZYsWaLFixd3uL6hoUGjR4/W1KlTdcMNN2jp0qW64IIL9MADD0SPNZ4/f76mTJmiu+66Sz/4wQ/04osv6p577tFnn32mCRMmdPk5EhS74S/ly/Vtc/2ebwjAUmbt1aYMZ5PZZQCIC5+kwxP+KK2trSooKNA//vEPnXjiidHL999/f51wwgm6/fbbNXDgQP3yl7/U1VdfLUmqr69XUVGRnn322eie1Ltz66236tVXX+0UFB977DHdeOONqqioiC4mvv766/Xqq69qxYoVkiIhtrm5Wf/617+iX3fIIYdo33331eOPP97l58nQczfkZDD0DNhRMMzQM2AfyVnEEgwGFQqFOnUGc3Jy9MEHH6i8vFwVFRWaOnVq9Dqv16uDDz5YCxbs+dCI3VmwYIGmTJkSDYlS5DjklStXqra2NnqbHR97+226+9gExW7IzWAFFWBHQeYoAjYSe1/MeCsoKNDkyZN1++23a9OmTQqFQnr++ee1YMECbd68WRUVFZKkoqKiDl9XVFQUva6nKioqYt7v9ut2d5vuPjZBsRtyXQRFwI4CYV4KAfvIStoj/elPf5JhGCotLVVWVpYeeughnXXWWZ1OsduVE044Qfn5+crPz9f48eMTXG3PMJbaDQw9A/bUHiIoAvaRnI6iJI0YMULvvfeempub1dDQoJKSEp1xxhkaPny4iouLJUmVlZUqKfnuJJ/KysroSuannnpKra2tkhQ9nrgriouLVVnZ8aCA7Z9vf9xd3Wb79V3Fq2M3MPQM2FM7HUXARpLXUdwuLy9PJSUlqq2t1dy5c3XSSSdp2LBhKi4u1ltvvRW9XUNDgxYuXKjJkydLkkpLSzVy5EiNHDlSQ4d23uR9VyZPnqz3339fgcB3Z5C++eabGjNmjAoLC6O32fGxt99m+2N3Fa+O3ZDD0DNgS63B5Oy5BiAZktdRnDt3rt544w2Vl5frzTff1NFHH62xY8fq/PPPl8Ph0JVXXqk77rhDr732mpYuXaqf/vSnGjhwoE4++eTd3u/XX3+txYsXq6KiQq2trVq8eLEWL14sv98vSTr77LPldrt14YUX6quvvtLLL7+sBx98UL/4xS+i9/Hzn/9cb7zxhn77299qxYoVuvXWW/XJJ5/o8ssv79ZzZCy1G3IZegZsqSVodgUA4id5HcX6+nrdcMMN2rBhg/r06aPTTjtNd955Z3QY+dprr1Vzc7MuueQS1dXV6fDDD9cbb7yx2z0UJemiiy7Se++9F/180qRJkqTy8nKVlZXJ6/Vq3rx5mjlzpvbff3/169dPN998c3QPRUk69NBD9eKLL+rXv/61fvWrX2nUqFF69dVXu7WHosQ+it3SFgrq0eWfmF0GgDg7qF+mDi/ebHYZAOLiOJkx/GxXDD13Q7YrQy4HQ1SA3TQH+X0ZsAenCInxRVDspoJM/gECdtMYICgC9pBjdgG2Q1DsJq+boAjYDUERsItcswuwHYJiN/ncu5+ACsB6Gvxhs0sAEBd0FOONoNhNdBQB+wlJMjjGD7ABOorxRlDsJi8dRcCWDLFPKmB9dBTjjaDYTT4WswC2FDLYJxWwPoJivBEUu4mOImBPoTBDz4D1ERTjjaDYTW6XSzkuOg+A3QSZowhYnFMSzZx4Iyj2ACufAfvxhwiKgLVlS+JQjHgjKPYAK58B+/GHeYMBrK3A7AJsiaDYA8xTBOynLcTLIWBtBMVE4JWxBxh6BuynNWR2BQB6J9/sAmyJoNgDfbIIioDdtAYZegasjY5iIhAUe6Bfdh7TZQGbaQ6aXQGAnnOIjmJiEBR7INPpVCFdRcBWmoKG2SUA6LFcSexckAgExR7qn51ndgkA4qgpYHYFAHqOYedEISj2UP9sDh4H7KQhEDa7BAA9RlBMFIJiDxEUAXtp9IdkMPoMWBTzExOFoNhDDD0D9hKWQxLHcwLWREcxUQiKPZSf6VauK9PsMgDEkUFQBCzIKTqKiUNQ7AWGnwF7CRkERcB6PCLOJA7f2V7on0NQBOwkFGZ7DcB6fGYXYGsExV5gniJgLwGDoAhYj8/sAmyNoNgLDD0D9hIIERQB6yk0uwBbIyj2Qp+sHLmdvLEAdtEe5nBOwFoyJTG6l0gExV5wOhwqyWWlFWAX7SFeEgFr8ZldgO3xqthLpbns3QTYRWvI7AoAdA/DzolGUOyl0jyP2SUAiJOWIEPPgLX4zC7A9giKvVSSky+XgzcXwA5aAmZXAKB7fGYXYHsExV7KcDpVlMNEWsAOmkIc9gxYR54kt9lF2B5BMQ5Kcxl+BuygMUBQBKyjj9kFpAWCYhyU5rGgBbCDRj9BEbCOvmYXkBYIinEwMLdAzFIErK8hEJJBVgQsop/ZBaQFgmIcZLsy1JdTWgDLM+SQlGF2GQD2KF9SttlFpAWCYpywnyJgDwZBEbAAuonJQlCMk0HMUwRsIWQQFIHUR1BMFoJinAzO85pdAoA4CIUJikBqc4iFLMlDUIyT3IxM9lMEbCBg8LIIpDavpEyzi0gbvCLG0fACzpwErM4fcpldAoDdYtg5mQiKcTSswGd2CQB6yR/mZRFIbQTFZOIVMY6KsvOUl0E7HLCythC7ogKpyymJ0btkIijGkcPhUFm+z+wyAPRCW5CgCKSufpKYHpJMBMU4Y/gZsLaWkNkVANi1YrMLSDsExTgbmu+V00FHArCq5oDZFQDYtSKzC0g7BMU4y3JlaCCntACW1RzksGcgNRVKyjK7iLRDUEyA4Qw/A5bVGCAoAqmJbqIZCIoJMCyfFVmAVTUEwmaXACAm5ieagaCYAH2zc+TNpD0OWFFjICyDpiKQYvK3fSDZCIoJMtLTx+wSAPSAIYckznsGUgvDzmYhKCbIWB8HlgNWZXCOLJBiGHY2C0ExQYpy8uVzZ5tdBoAeCBls6AukjixJPrOLSFsExQQa66WrCFhRMMzQM5A6iiWxP7FZCIoJNMbLweWAFQXDdBSB1FFqdgFpjaCYQH2zc9Q/O9fsMgB0kz/MSyOQGnIlsTjUTLwaJtgYhp8By2kPM8wFpAa6iWYjKCbYWIafActpD/HSCKQGgqLZeDVMMI87SwNz2SQUsJLWIB1FwHxescm2+QiKScCiFsBaWoNmVwBAGmR2ARBBMSlGe/uwsB+wkOYgZ/gB5nJIGmh2ERBBMSnyMtwaku81uwwAXdRERxEwWX9FNtqG2QiKSTKxcIDZJQDoosYAHUXAXCxiSRUExSQZ4SlUbgbnxwJW0OAPm10CkMYyxNnOqYOgmCQuh1MTfP3NLgNAFzQGwzJoKgImKZXE6UipgqCYRBP7DGBRC2AJDkmMAADmKDO7AOyAoJhEXne2hrKoBbCEsDLMLgFIQ30kFZhdBHZAUEyyvfsUmV0CgC4IGwx9Ack31OwCsBOCYpINLyhUfobb7DIA7EEwTEcRSC63pBKzi8BOCIpJ5nQ4NKGQRS1AqguE6SgCyTVExJLUw9+ICVjUAqS+QJiXRyC5hphdAGLgldAEBZlZGl5QaHYZAHajnaAIJNEASblmF4EYeCU0ycQ+nNQCpLK2EH1/IHlYxJKqCIomGZbvk8+dbXYZAHahLUhQBJIjV5GOIlIRQdEkDodD+/fliCIgVbUEza4ASBfDJGbupyyCoon2KhygHBdbcACpiKAIJINbLGJJbQRFE2U6ndqHDbiBlNQU5LBnIPHKxLnOqY2gaLJ9+xbL5aDlDqSaBj9BEUgslzjXOfURFE2Wm5Gp8T424AZSTWMgbHYJgM0NVmToGamMoJgCDug3kGm8QIppDIZl0FQEEsQhabjZRaALCIopwJeVrdHevmaXAaADh6RMs4sAbKpEbLBtDQTFFHFQ/1KzSwCwk7DYlQBIjJFmF4AuIiimiP7ZuRpe4DO7DAA7CBsERSD++kvymF0EuoigmELoKgKpJRhm2w4g/kaYXQC6gaCYQgbmFmhwHr9lAakiEOYlEoivvpL6mV0EuoFXwRRzeNFgs0sAsI2fjiIQZ2PNLgDdRFBMMSW5BRpRUGh2GQAk+ekoAnE0QBLvb1bDq2AKOqxoMPsqAimgLchPIhA/Y8wuAD1AUExB/bJzNdbLHA7AbK0hgiIQHyWSvGYXgR4gKKaoyUWD5OQMaMBUrUGzKwDswCG6idZFUExRPne2JhYOMLsMIK01ERSBOCiVlG92EeghgmIKO6R/qTIc/BUBZmkKcNgz0DsOSaPNLgK9QApJYXmZbk3qW2x2GUDaagqEzS4BsLgh4kxnayMoprgD+w9UlpO93AAz1NNRBHrBJWmU2UWglwiKKS7blaED+g00uwwgLTUHwzIMFpUBPTNSUrbZRaCXCIoWsF+/YuVnuM0uA0hDDkkZZhcBWFCOpOFmF4E4IChaQKbTpSNLhphdBpCWwgZBEei+cYoMPcPqCIoWMcbbT4PyPGaXAaSdEEER6KY+kpgyZRcERQs5pqRMTg73A5IqZNAVAbpngtkFII4IihbSLztX+/QtMrsMIK0EwgRFoOuGSGL0y04IihZz6IBBynVlml0GkDb8YV4mga7JEEf12Q+vgBaT5crQ4cWDzS4DSBvtIV4mga4ZLSnL7CIQZ7wCWtB4X3+V5HBuJpAMbSHmBQN7liepzOwikAAERQtyOBw6emAZy1qAJCAoAl0xUUQKe+Jv1aKKc/I1oXCA2WUAttcSNLsCINUNltTP7CKQIARFCzu8aLCyXezxBiRSM0ER2I0sRTbXhl0RFC0sJyNTRxYPNbsMwNaaAobZJQApbLwkjpi1M4KixY0v7K9h+T6zywBsqzEQNrsEIEUNECew2B9B0Qamlg5XlpNNgYFEaKCjCMSQocgCFtgdQdEGCjLdmsIQNJAQzUFDhsHKZ6CjMZJyzC4CSUBQtImJfQZoaL7X7DIAm2LRGPAdn9gzMX0QFG3k+IHD5WYIGoi7sEFQBCIckvbe9ifSAUHRRgrcWZpSPMTsMgDbCREUgW1GS/KYXQSSiKBoM3v3KdKQPIaggXgKGXTqAalQ0kizi0CSERRt6PjS4cp08lcLxEsgTFBEusuQtK8Yck4/pAkb8rizNKWIIWggXvxhXiqR7sZLyjO7CJiAVz+b2rtPkcpYBQ3ERXuIl0qks2JFznNGOuLVz6YcDoe+N2ik8jIyzS4FsLy2EMNtSFdZiqxyRroiKNpYbkamThg0khklQC+1BvkpQrraR5zlnN4IijY3JN+rg/qXml0GYGktIbMrAMxQpsh5zkhnBMU0MHnAIJXmFphdBmBZzUGzKwCSLV/SOLOLQAogKKYBp8Oh7w8aqWwXmwYDPdEcMMwuAUgil6T9tv2JdEdQTBMF7iwdXzrc7DIAS2oIhM0uAUiiieL0FWxHUEwjIz19NKlvsdllAJZDUET6GCJpkNlFIIUQFNPMlKIhGpDNpqlAd7QEJcNg5TPszqvIxtrAdwiKacbldOrEwSM54g/oNvYkhZ1lStpfzEvEzkgLaagwK0fTSkeYXQZgKWGDxWCws30l5ZpdBFIQQTFNjfb21cHsrwh0Wcig0wK7GimpyOwikKIIimns0AGDNKKg0OwyAEsI0lGELfWTNMbsIpDCCIppzOFw6IRBI9U3K8fsUoCUFwjzcgm7yZY0SeKgV+wGr3xpzu1y6aShY9iMG9gDP0ERtuKSdKCkLLMLQYrjlQ/yubP1g8Gj5OS3SmCX2kO8XMJOJimyHQ6we7zyQZI0JN+rI0uGml0GkLLaQ/wiBbsYJ4nDF9A1BEVETepbrAmFA8wuA0hJLUGCIuxgsCS2R0PXERTRwbElZRqYW2B2GUDKaQmaXQHQW30VOccZ6DqCIjpwOZ2aPmS0PJlus0sBUkpL0DC7BKAX8hQ5eYW3fXQP/2LQSW5Gpk4tG8dKaGAHjXQUYVmZiqxwpgGA7iMoIqY+WTk6ZegYZTj4JwJIUlOAjiKsyKFIJzHf7EJgUaQA7FJJboF+MIRtcwBJavCHzC4B6IF9FDl9BegZgiJ2a3hBoY4rHW52GYDpWkKSYfBLE6xkL0mDzC4CFkdQxB6NL+yvw4sGm10GkAIyzS4A6KJRkvglH71HUESXHNS/VPv1ZYNWpLewwQIvWMFQSWPMLgI2QVBElx1ZPFRjvX3NLgMwTchwmV0CsAcDJU0wuwjYCEERXeZwODRt0AgNzeN8UKSnIB1FpLT+kvaVWICIOCIooltcDqd+OGS0inLyzC4FSLpAmJdMpKpCSQeIt3XEG/+i0G1ul0unDh2nAdm5ZpcCJJWfoIiUVKDIhtpMjUD88aqHHsnJyNBpZXsRFpFW2kO8ZCLV5Ek6WJy6gkThVQ89RlhEumkLMfcLqSRP0mRJ2WYXAhsjKKJXCItIJ61BgiJSRa6kQ0RIRKIRFNFrhEWki5ag2RUAUiQkTpaUY3YhSAMERcQFYRHpoDlomF0C0t724WZCIpKDoIi4ISzC7poCZleA9JYvQiKSjaCIuCIsws4aA3QUYZYCsXAFZiAoIu62h0U25YbdNATCZpeAtORRJCRmmV0I0hBBEQmRk5GhH5XtpcF5HrNLAeKmNWTIMHjZRDL1USQksk8izMErHhLG7XLplKFjNcrTx+xSgLgxxHnPSJZiRTbTzjS7EKQxgiISKsPp1A8Gj9LEwgFmlwLEhWEQFJEMQyTtL47lg9kIikg4h8Oh40qH66B+A80uBei1EEERCTdK0t6S2OAd5iMoImkOLx6io0vKeOmDpQUNOjxIpImSxphdBBBFUERSTepbrB8MHi2Xg7gIawqECYpIBKciQ81DzS4E6ICgiKQb5e2jGWXjlOXiDRfW4w/xsol4y1Bk0UqJ2YUAnfCKB1OU5nl05rAJ8mSy5QOspT1MNxzxlCPpUEl9zS4EiImgCNP0zc7R2SMmqjS3wOxSgC5rCxEUES+Fkg5XZENtIDURFGGq3IxMzSgbpwlsnwOLaAkSFBEPg8VpK7ACgiJM53I6dXzpcB1dUiYna6KR4lqDZlcAa3NI2kvSPuItGFbAv1KkjEl9i3Vq2Vhlu9inDqmriaCIHsuQdKCk4WYXAnQZQREpZUi+Vz8eMUF9s3LMLgWIqSkQNrsEWFKeIvMRmWYDayEoIuV43dk6a/gEjSgoNLsUoJPGgNkVwHr6STpMUr7ZhQDdRlBESnK7XJo+ZLQO7l9qdilABw1+OorojmGSDpLEVmCwJoIiUpbD4dBhRYP1g8GjlOnknypSQ1vYkGHw7xF7kqHISSvjxVstrIxVA0h5o7191T87V/9ev1pVbS1mlwPIUIYc8ptdBlKWV9J+isxLBKyNX3NgCYVZOTpz+ATt26fY7FIAhQ1+x8aulCkyH5GQCHsgKMIyMpxOHTOwTNOHjGYLHZiKoIjOMhTpIk4Qb62wE17tYDkjPX1UlJ2n1zd8rY0tjWaXgzQUMFwsTcAOPIrMR6SLCPvh1x5YUoE7Sz8atpcO7l/KWS5IukCYl05sN0QMNcPOeLWDZTm3rYo+rWyc8jIyzS4HacQfcpldAkznVqSLuLck/j3AvgiKsLwh+V6dM3JvDc33ml0K0kR7mD52eiuSdKSkErMLARKOoAhbyM3I1KlDx+qYkjL2XETCtQUJiukpQ9I+ipzXnGVyLUBysJgFtuFwOLRv32INK/Bp3sZvtL65weySYFOtIYJi+umrSEjMNbsQIKlovcB2vO5szSgbp6kDh8ntZO4Q4q8laHYFSB6npL0kHSJCItIRHUXYksPh0N59ilSW79Obm77RuqZ6s0uCjTQHzK4AyeGVNElSvtmFAKahowhb87izdFrZOB03cLiy6C4iTpqChtklIKGcksYqsu0NIRHpjY4i0sLEPgNUVuDVmxvLtbapzuxyYHENgbDZJSBh+ityugr7IgISHUWkkYLMLJ1aNlbTSkcoy0V3ET3X4KejaD9ZihzBd7AIicB36Cgi7Ywv7K/hBT79t3K9vqqtEm/56K72sCHDcMrhoLNoD0MVGWpm435gZw7DMHifRNqqaG3S25vWqqK1yexSYDFXjm+Q0+E3uwz0ikfSREmFZhcCpCyCItKeYRj6qm6LPqhYr5YQy1nRNbP2alWGs9nsMtAjLkmjJQ0TM7CA3WPoGWnP4XBoQuEAjfL00fyqDfqiplJhBqSxByHDxQuoJZUqMsycY3YhgCXQUQR2Ut3Worc3r9UGTnbBbvxsTEh5mbVml4Eu66PIxtk+k+sArIWgCOzCirpqvV/xrZqCzENDZxeMknxZ1WaXgT3KlTROUonZhQCWxMgJsAtjff00wlOoz6or9En1JrWHQ2aXhBTiDzO3LbVlSholqUzMQwR6jo4i0AVtoaAWbdmkz2sqFDTYEgXSj4a5NDiv0uwy0IlDke1uRktym1wLYH0ERaAbmgJ+LdyyUUtrqxTmRyet/XBwhkZ5K8wuAx2USBojjt0D4oegCPRAnb9NCyo3aEV9Neuj09SxAzO1T5/NZpcBSVKxIh1Ej9mFALZDUAR6YUtbi+ZXrteaRla/ppvJAzI1eQBB0VxFinQQCYhAohAUgTjY1NKoDyvXaz1b6qSNvQszNbWUoGiOIkU6iF6zCwFsj6AIxNGmlkYt2rKJDmMaGF6QoZOHMkcxuQYoEhB9JtcBpA+CIpAAW9tbtWjLJq2or1aIHzFb6p/l1DmjqswuI00USxohzmQGko+gCCRQY8Cvz2o2a+nWKvnZh9FW3E7p8r3YcDtxXJIGK3Iec57JtQDpi6AIJEFbKKglWyv1WU2FWoIBs8tBnFw1fqscDvbVjK8sRTbJHir2QQTMR1AEkigYDmtZ3RZ9Ur1Zdf42s8tBL105vlFOR7vZZdhEgaThkkrFSSpA6iAoAiYwDEPlTXVasrVK5Y217MVoUbP2alWGs9nsMiyunyIBcYDZhQCIgbOeARM4HA4NLyjU8IJCNfrbtaS2Sl/WVqmZYWlLCRkuXkR7xC1pkKQh4hQVILXRUQRSRNgwtKaxVku2VmpdU73Z5aALfjYmpLxMtkLqun6KhMNiMbwMWAO/DAMpwulwaJSnj0Z5+qjO36alW6v0Ve0WtYToMqaqQNhldgkWkKXvuoesXgasho4ikMJC4bC+btyqpVu3aH1zPXMZU8xPRjg1IIe9FGOjewjYAR1FIIW5nE6N8fbTGG8/NQX8WlFfrRV11apqazG7NEhqCznMLiHFeBVZtTxQUrbJtQCIBzqKgAXVtLVoeX21VtbVqD7A9ixm+cHgTI32pvt5z/n6LhwytAzYDUERsLiKliatrK/RqoYaNQb8ZpeTVo4tydQ+fdMxKOYoEgxLJXlMrgVAIhEUAZswDEObW5u0qr5GXzdsVQOhMeEOGZCpQwekS1DMlVQkqUSRM5cZdgfSAUERsKnqthaVN9apvLFWm1qaFGYpTNxNLMzQcaUVZpeRIA5FAmHRtg/2OwTSEUERSANtoaDWNdWrvLFWaxvr2XInToblZ+iUMjsFxUxJ/RUJhv3FWcsAWPUMpIFsV4bGePtqjLevDMNQRWuzyhtrVd5Up8pWjqDrqcZA2OwS4sCjyFY2AyT1EVvZANgRHUUgzTUH/Vrf1KCNLY3a2Nyo6na23ukqt1O6fK9qs8vopgJFgmFfRYIhXUMAu0ZQBNBBWyioTc2N2tDSoI3Njapsa1aYl4ldumr8VjkcqdxZLFAkFG7/IBgC6DqGngF0kO3K0HBPoYZ7CiVJgXBIm1uaoh3Hza2NCoRTORgll6FMOZQqe1lmSvIpsvG1T3QMAfQWQRHAbmU6XRqS79WQfK8kKWwYqmlv1ZbWZm1pa9GWtsifraGgyZWaI2xkyOkwIyhmKBIIt4dCnyJb2ABA/BAUAXSL0+FQ/+xc9c/uGEoaA/5IaGyNhMeqthbV+9tsvylPyHAl+IXUocgG1/nbPjyKhMI8sZchgEQjKAKIi4JMtwoy3RpeUBi9LBAOaUtbi7a2t6rO36Z6f3v0zzabdCCDYZeyXPG4J5ci4S9/p4+8bdcBQPIRFAEkTKbTpYG5BRqYW9DpuvZQsENwrPe3qW7bn40Bv2U2CPeHXV084ThTUrYi3cGcHf4/W5EwmC06hABSDUERgCmyXBkakJOhATmdY5ZhGGoLhdQaCqglGPloDQUjf3b6PKjWUCDpsTLD4VSWyyV/yKXICSZuRcKge9tHljqGQV5uAVgP2+MAsDzDMBQyDAWNsILhsIJGWKFtf0Y+NxQMhxXa4XpDklMOOR3bPySnnHI6JIfDsdN1DrkcTmU5XcpyueR2ueRysDE1APsjKAIAACAmfiUGAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMREUAQAAEBMBEUAAADERFAEAABATARFAAAAxERQBAAAQEwERQAAAMT0/wGjk60KTnAIbwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# 根据分段生成标签\n",
    "bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    "labels = ['0-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100']\n",
    "\n",
    "# 将数据分组\n",
    "df_sorted['score_group'] = pd.cut(df_sorted['jaro_similarity'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# 统计每个组的频数\n",
    "group_counts = df_sorted['score_group'].value_counts()\n",
    "\n",
    "# 生成饼状图\n",
    "plt.figure(figsize=(8, 8))\n",
    "group_counts.plot(kind='pie', autopct='%1.1f%%', startangle=90, cmap='Set3')\n",
    "plt.title('Score Distribution')\n",
    "plt.ylabel('')  # 去掉 y 轴标签\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "54d67932-edcd-450c-8430-a075009a4f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_24812\\3673326524.py:10: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  percentage = group_percentage[i]\n"
     ]
    },
    {
     "data": {
      "image/png": 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vL4sxXb16VT179lSbNm0yjXf58uWKiIjQqFGjtHfvXvn5+Sk4ODjL0JfBMAzNnDlTffr0ydQ2duxYizG9+uqrZltaWppcXFz02muvKTAwMM9zmpKSoqeffloDBw7Msn38+PG6du2a9u7dq5YtW6pfv35m244dO7Rz504NGjQo0369e/fWkiVLdPny5TyPBQAAALAqBv624sWLG5999plhGIaxdu1aw9bW1khISDDbr169atjY2BjffvutIcmYNm2a4eXlZaSlpZk1Bw4cMCQZx48fz/IYFy5cMBwcHIxFixZlauvevbsxYsQIY9SoUYafn59FW+PGjY2wsDDzfVpamlG2bFljwoQJ2X6e3bt3G7a2tkZiYqLF9ooVKxpTp07Ndr+7hYaGGh07dsxTbYb58+cbHh4emba3a9fOmD17tmEYhnHkyBHD1dXVMAzDSElJMfz8/Izdu3dn26ePj4/53wYAAACwFgkJCYYki9yQFc6w/Q1paWlatmyZkpKSFBAQIElKTk6WjY2NnJyczDpnZ2fZ2tpqx44dku6cUXJ0dJSt7f9Nv4uLiyRp+/btWR5r0aJFcnV1Vbdu3Sy2z58/X7///nuWl/6lpKQoOjra4myXra2tAgMDFRUVle3n2rZtm6pVq6aiRYtmaps4caJKliyp+vXr6/3339ft27ez7aeg+Pn5adOmTbp9+7bWrl2runXrSpImTZqkli1bqmHDhtnu27hxY23btu2BjxEAAAB4EAhs+XDw4EG5ubnJyclJAwYM0DfffKOaNWtKkpo2baoiRYrorbfe0o0bN5SUlKQ333xTaWlpio+PlyS1aNFC8fHxev/995WSkqIrV65o2LBhku5cLpmVuXPnqkePHmawk6Tjx49r2LBhWrx4sezt7TPtc+nSJaWlpal06dIW20uXLm2OJSunT59W2bJlM21/7bXXtGzZMm3evFkvv/yy/vOf/2jo0KG5zNbfN2zYMNnb26ty5cr65ptvNHfuXB0/flwLFy7UO++8owEDBujRRx/VM888o4SEBIt9y5Ytq9OnTz/wMQIAAAAPAoEtH6pXr66YmBjt3LlTAwcOVGhoqI4cOSJJKlWqlFauXKkffvhBbm5u8vDw0NWrV9WgQQPzjJqvr68WLlyoyZMny9XVVd7e3vLx8VHp0qUtzrpliIqK0tGjRy3uKUtLS1OPHj00ZswYVatWrUA/382bN+Xs7Jxpe0REhFq2bKm6detqwIABmjx5smbMmKHk5OQCPf69PDw8tHTpUp0+fVpbt25VzZo19fLLL+v999/XkiVL9Pvvvys2Nlaurq4aO3asxb4uLi66cePGAx0fAAAA8KAQ2PLB0dFRVapUkb+/vyZMmCA/Pz9Nnz7dbA8KCtKJEyd04cIFXbp0SZ9//rnOnDmjSpUqmTU9evRQfHy8zpw5o7/++kujR4/WxYsX9eijj2Y63meffaZ69erJ39/f3Hbt2jXt2bNH4eHhsre3l729vcaOHav9+/fL3t5emzZtkqenp+zs7DKtUHn+/Hl5e3tn+/k8PT115cqVXOehSZMmun37tk6dOpVrbUGaP3++ihUrpo4dO2rLli3q1KmTHBwc9PTTT2vLli0WtZcvX1apUqX+0fEBAAAABYXAVgDS09OzPMvk6empYsWKadOmTbpw4YLat2+fqaZ06dJyc3PT8uXL5ezsrCeffNKi/fr161qxYkWmFRvd3d118OBBxcTEmK8BAwaYZ/+aNGkiR0dH+fv7a+PGjRZj3bhxo3nPXVbq16+vY8eOyTCMHD93TEyMbG1tM61a+SBdvHhRY8eO1YwZMyTdOdOYmpoqSUpNTVVaWppF/aFDh1S/fv1/bHwAAABAQbKawDZx4kTZ2NhYLM9+69YthYWFqWTJknJzc1PXrl0znS2Ki4tTSEiIXF1d5eXlpSFDhmRaCGPLli1q0KCBnJycVKVKFS1YsCDT8XN7VlnGWFxcXOTi4qK2bdtqy5YtGj58uLZs2aLnn3/erJ0/f7527NihEydOaPHixXr66ac1ePBgVa1a1ayZOXOm9u7dq19//VWzZs1SeHi4JkyYkOk5ZMuXL9ft27f1wgsvWGy3tbVV7dq1LV5eXl5ydnZW7dq1VaRIEUl3LmP89NNPtXDhQh09elQDBw5UUlKSevfune1/i1atWun69evmowqkO5dlTps2Tfv379fvv/+uJUuWaPDgwXrhhRdUvHhxs+7IkSOKiYnR5cuXlZCQYIbJDLt27VKNGjV05swZc1tcXJxiYmIUFxentLQ0c5+7n0uXYdCgQXrjjTf0yCOPSJKaN2+uzz//XEePHtUnn3yi5s2bm7U3btxQdHS0goKCsv2sAAAAgFX7ZxatzNmuXbuMSpUqGXXr1jVef/11c/uAAQOM8uXLGxs3bjT27NljNG3a1GjWrJnZfvv2baN27dpGYGCgsW/fPuPHH380PD09jeHDh5s1v//+u+Hq6mpEREQYR44cMWbMmGHY2dkZa9asMWuWLVtmODo6GvPmzTMOHz5s9OvXzyhWrJhx/vz5TGNp27atUaZMGcPGxsawt7c32rRpY6xbt87i87z11ltG6dKlDQcHB6Nq1arG5MmTjfT0dIulO1988UWjRIkShqOjo1G3bt0sl+s3DMMICAgwevTokad5zGpZf8MwjBkzZhgVKlQwHB0djcaNGxs7duzIta9nnnnGGDZsmPk+OjraaNKkieHh4WE4Ozsbvr6+xn/+8x/j1q1bFvtVrFjRkJTplWHz5s2GJOPkyZPmttDQ0Cz32bx5s0Xfa9asMRo3bmzxOISkpCTj6aefNooWLWq0adPG4r/Z0qVLjerVq+f6WQEAAIB/Wl6X9S/0wHbt2jWjatWqxvr1640nnnjCDGxXr141HBwcjJUrV5q1R48eNSQZUVFRhmEYxo8//mjY2toa8fHxZs3s2bMNd3d3Izk52TAMwxg6dKhRq1Yti2N2797dCA4ONt/n9qyyvIwlL/L6H8Ua7N+/3/Dy8jKuXbtW2EPJtyZNmhhLliwp7GEAAAAAmeQ1G2ReC/4fFhYWppCQEAUGBurdd981t0dHRys1NdXiGWI1atRQhQoVFBUVpaZNmyoqKkp16tSxWLY+ODhYAwcO1OHDh1W/fn1FRUVZ9JFRk3HpZcazyoYPH2623/ussryMJSvJyckW97YlJibmY4ayFxcXp0uXLhVon3cbOHCgVq9ebXEpZ155enqqQoUKD2BUeXPp0iV16dJFzz33XKGNAQAAAPi7CjWwLVu2THv37tXu3bsztcXHx8vR0THTPV13P0MsPj4+y2eMZbTlVJOYmKibN2/qypUr2T6r7NixY3keS1YmTJigMWPGZNv+d8TFxamGr69uWumS9S6urjp29GihhTZPT89/5BlxAAAAwINUaIHtjz/+0Ouvv67169dn+cyv/wXDhw9XRESE+T4xMVHly5cvkL4vXbqkmzdu6Jl3Z8vL5/7PgD1IF04e14oRA3Xp0qVCPcsGAAAA/LcrtMAWHR2tCxcuqEGDBua2tLQ0/fzzz5o5c6bWrl2rlJQUXb161eLM1t3PEPP29s60mmPGKpJ312T1HDJ3d3e5uLjIzs4u12eVeXt75zqWrDg5OcnJySmPM5I/Xj5V9Yiv3wM9BgAAAIDCUWjL+rdp0ybTc8QaNmyo559/3vz/Dg4OFs8Qi42NVVxcnPkMsYCAAB08eFAXLlwwa9avXy93d3fVrFnTrLm7j4yajD7y8qwyf3//XMcCAAAAAAWt0M6wFS1aVLVr17bYVqRIEZUsWdLc3qdPH0VERKhEiRJyd3fXq6++qoCAAHORj6CgINWsWVMvvviiJk2apPj4eI0YMUJhYWHmma0BAwZo5syZGjp0qF566SVt2rRJK1as0OrVq83jRkREKDQ0VA0bNlTjxo01bdo0i2eVeXh45DoWAAAAAChohb5KZE6mTp0qW1tbde3aVcnJyQoODtZHH31kttvZ2WnVqlUaOHCgAgICVKRIEYWGhmrs2LFmjY+Pj1avXq3Bgwdr+vTpKleunD777DMFBwebNd27d9fFixc1cuRIxcfHq169elqzZo3FQiS5jQUAAAAACpqNYRhGYQ/iYZGYmCgPDw8lJCTI3d39b/W1d+9e+fv7K3zJBqu7h+3M0f2a+XygoqOjLe5RBAAAAHBHXrNBod3DBgAAAADIGYENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0oILGxsfL29ta1a9cKeyj5MmzYML366quFPQwAAADchcCGB2727NmqW7eu3N3d5e7uroCAAP30008WNS1btpSNjY3Fa8CAARY197bb2Nho2bJlWR7zl19+kb29verVq2exvVKlSln2ExYWJkm6fPmyXn31VVWvXl0uLi6qUKGCXnvtNSUkJOT6OYcPH65XX31VRYsWzdT222+/qWjRoipWrJjF9gULFmQai7Ozc67Hyu1zLlmyROXLl1fx4sUVERFh0Xbq1ClVq1ZNiYmJFtvffPNNLVy4UL///nuejw8AAIAHy76wB4D/feXKldPEiRNVtWpVGYahhQsXqmPHjtq3b59q1apl1vXr109jx44137u6umbqa/78+Wrbtq35/t4AJElXr15Vz5491aZNG50/f96ibffu3UpLSzPfHzp0SE8++aSefvppSdLZs2d19uxZffDBB6pZs6ZOnz6tAQMG6OzZs/ryyy+z/YxxcXFatWqVZsyYkaktNTVVzz33nB5//HFFRkZmand3d1dsbKz53sbGJtvj5OVzXrp0SX379tWCBQv06KOPKiQkRK1bt9a//vUvSdIrr7yiiRMnyt3d3aI/T09PBQcHa/bs2Xr//ffzNAYAAAA8WAQ2PHAdOnSweD9+/HjNnj1bO3bssAhsrq6u8vb2zrGvYsWK5VozYMAA9ejRQ3Z2dvr2228t2kqVKmXxfuLEiapcubKeeOIJSVLt2rX11Vdfme2VK1fW+PHj9cILL+j27duyt8/6j8yKFSvk5+enRx55JFPbiBEjVKNGDbVp0ybLwGZjY5PrZ8pKdp/z999/l4eHh7p37y5JatWqlY4ePap//etf+uKLL+Tg4KAuXbpk2WeHDh309ttvE9gAAACsBJdE4h+VlpamZcuWKSkpSQEBARZtS5Yskaenp2rXrq3hw4frxo0bmfYPCwuTp6enGjdurHnz5skwDIv2+fPn6/fff9eoUaNyHUtKSooWL16sl156KcezWgkJCXJ3d882rEnStm3b1LBhw0zbN23apJUrV2rWrFnZ7nv9+nVVrFhR5cuXV8eOHXX48OFcx57T56xatapu3Lihffv26fLly9q9e7fq1q2rK1eu6J133tHMmTOz7bdx48b6888/derUqVzHAAAAgAePM2z4Rxw8eFABAQG6deuW3Nzc9M0336hmzZpme48ePVSxYkWVLVtWBw4c0FtvvaXY2Fh9/fXXZs3YsWPVunVrubq6at26dXrllVd0/fp1vfbaa5Kk48ePa9iwYdq2bVuO4SrDt99+q6tXr6pXr17Z1ly6dEnjxo1T//79c+zr9OnTmQLbX3/9pV69emnx4sWZLj/MUL16dc2bN09169ZVQkKCPvjgAzVr1kyHDx9WuXLlstwnt89ZvHhxLVy4UD179tTNmzfVs2dPBQcHq0+fPgoPD9fJkyf11FNPKTU1VaNHj1a3bt3MfcuWLWt+nkqVKuX4mQEAAPDgEdjwj6hevbpiYmKUkJCgL7/8UqGhodq6dasZ2u4ORHXq1FGZMmXUpk0bnThxQpUrV5YkvfPOO2ZN/fr1lZSUpPfff1+vvfaa0tLS1KNHD40ZM0bVqlXL05jmzp2rdu3amSHlXomJiQoJCVHNmjU1evToHPu6efNmpsVC+vXrpx49eqhFixbZ7hcQEGBxprFZs2by9fXVxx9/rHHjxmWqz+vn7Ny5szp37my+37p1qw4cOKAZM2aoSpUq+uKLL+Tt7a3GjRurRYsW8vLykiS5uLhIUpZnNwEAAPDP45JI/CMcHR1VpUoV+fv7a8KECfLz89P06dOzrW/SpImkO6sr5lTz559/Kjk5WdeuXdOePXsUHh4ue3t72dvba+zYsdq/f7/s7e21adMmi31Pnz6tDRs2qG/fvln2fe3aNbVt21ZFixbVN998IwcHhxw/n6enp65cuWKxbdOmTfrggw/M8fTp00cJCQmyt7fXvHnzsuzHwcFB9evXz/Zz3+/nlKTk5GS98sor+vjjj/Xbb7/p9u3beuKJJ1S9enVVq1ZNO3fuNGsvX74sKfO9fgAAACgcnGFDoUhPT1dycnK27TExMZKkMmXK5FhTvHhxOTk5ycHBQQcPHrRo/+ijj7Rp0yZ9+eWX8vHxsWibP3++vLy8FBISkqnfxMREBQcHy8nJSd9//32eltmvX7++jhw5YrEtKirKYkXK7777Tu+9954iIyOzXJxEunMG7eDBg2rfvn2W7e7u7vf1OSXp3XffVdu2bdWgQQPt27dPt2/fNttSU1MzrZrp4OBgsRgMAAAACg+BDQ/c8OHD1a5dO1WoUEHXrl3T0qVLtWXLFq1du1aSdOLECS1dulTt27dXyZIldeDAAQ0ePFgtWrRQ3bp1JUk//PCDzp8/r6ZNm8rZ2Vnr16/Xf/7zH7355puSJFtbW9WuXdviuF5eXnJ2ds60PT09XfPnz1doaGime8ASExMVFBSkGzduaPHixUpMTDSfV1aqVCnZ2dll+RmDg4PVt29fpaWlmTW+vr4WNXv27Mk0zrFjx6pp06aqUqWKrl69qvfff1+nT5+2OPM3fPhwnTlzRosWLbqvzylJR44c0fLly7Vv3z5JUo0aNWRra6u5c+fK29tbx44dU6NGjcz6bdu26fHHHzcvjQQAAEDhIrDhgbtw4YJ69uypc+fOycPDQ3Xr1tXatWv15JNPSrpzueSGDRs0bdo0JSUlqXz58uratatGjBhh9uHg4KBZs2Zp8ODBMgxDVapU0ZQpU9SvX7/7Hs+GDRsUFxenl156KVPb3r17zUsEq1SpYtF28uTJbBfiaNeunezt7bVhwwYFBwfneSxXrlxRv379FB8fr+LFi8vf31+RkZEWC7KcO3dOcXFxee4zg2EY6t+/v6ZMmaIiRYpIunOP2oIFCxQWFqbk5GTNnDnT4mzfsmXLcr1fDwAAAP8cG+PeddHxwCQmJsrDw8NcJv7v2Lt3r/z9/RW+ZIMe8fUroBEWjDNH92vm84GKjo5WgwYNCns4/5hZs2bp+++/N88c/rf56aef9MYbb+jAgQN5WmUTAAAA+ZfXbMBPZXjoxMXF6dKlSwXeb6NGjXTkyBFt27bNPKN1vzw9PVWhQoUCHlneJCUlaf78+YQ1AAAAK8JPZnioxMXFqYavr24+wGXrP/roo3zv6+LqqmNHjxZKaLv7eWwAAACwDgQ2PFQuXbqkmzdu6Jl3Z8vLp2phD8fChZPHtWLEQF26dKnQzrIBAADAuhRqYJs9e7Zmz56tU6dOSZJq1aqlkSNHql27dpKkli1bauvWrRb7vPzyy5ozZ475Pi4uTgMHDtTmzZvl5uam0NBQTZgwweKyri1btigiIkKHDx9W+fLlNWLECPXq1cui31mzZun9999XfHy8/Pz8NGPGDDVu3Nhsv3Xrlt544w0tW7ZMycnJCg4O1kcffaTSpUsX8Kzgn+DlU9Xq7v0DAAAA7lWoD84uV66cJk6cqOjoaO3Zs0etW7dWx44ddfjwYbOmX79+OnfunPmaNGmS2ZaWlqaQkBClpKQoMjJSCxcu1IIFCzRy5Eiz5uTJkwoJCVGrVq0UExOjQYMGqW/fvhYLQyxfvlwREREaNWqU9u7dKz8/PwUHB+vChQtmzeDBg/XDDz9o5cqV2rp1q86ePasuXbo84BkCAAAA8DAr1MDWoUMHtW/fXlWrVlW1atU0fvx4ubm5aceOHWaNq6urvL29zdfdK6isW7dOR44c0eLFi1WvXj21a9dO48aN06xZs5SSkiJJmjNnjnx8fDR58mT5+voqPDxc3bp109SpU81+MpaH7927t2rWrKk5c+bI1dVV8+bNkyQlJCRo7ty5mjJlilq3bi1/f3/Nnz9fkZGRFmMFAAAAgIJUqIHtbmlpaVq2bJmSkpIUEBBgbl+yZIk8PT1Vu3ZtDR8+XDfuWiwiKipKderUsbgsMTg4WImJieZZuqioKAUGBlocKzg4WFFRUZKklJQURUdHW9TY2toqMDDQrImOjlZqaqpFTY0aNVShQgWzJivJycnmg5fvfgAzAAAAAORFoS86cvDgQQUEBOjWrVtyc3PTN998Yz40uEePHqpYsaLKli2rAwcO6K233lJsbKy+/vprSVJ8fHyme8gy3sfHx+dYk5iYqJs3b+rKlStKS0vLsubYsWNmH46OjipWrFimmozjZGXChAkaM2bMfc4IAAAAANxR6IGtevXqiomJUUJCgr788kuFhoZq69atqlmzpvr372/W1alTR2XKlFGbNm104sQJVa5cuRBHnTfDhw9XRESE+T4xMVHly5cvxBEBAAAA+G9S6JdEOjo6qkqVKvL399eECRPk5+en6dOnZ1nbpEkTSdJvv/0mSfL29tb58+ctajLee3t751jj7u4uFxcXeXp6ys7OLsuau/tISUnR1atXs63JipOTk9zd3S1eAAAAAJBXhR7Y7pWenq7k5OQs22JiYiRJZcqUkSQFBATo4MGDFqs5rl+/Xu7u7uZllQEBAdq4caNFP+vXrzfvk3N0dJS/v79FTXp6ujZu3GjW+Pv7y8HBwaImNjZWcXFxFvfbAQAAAEBBKtRLIocPH6527dqpQoUKunbtmpYuXaotW7Zo7dq1OnHihJYuXar27durZMmSOnDggAYPHqwWLVqobt26kqSgoCDVrFlTL774oiZNmqT4+HiNGDFCYWFhcnJykiQNGDBAM2fO1NChQ/XSSy9p06ZNWrFihVavXm2OIyIiQqGhoWrYsKEaN26sadOmKSkpSb1795YkeXh4qE+fPoqIiFCJEiXk7u6uV199VQEBAWratOk/P3EAAAAAHgqFGtguXLignj176ty5c/Lw8FDdunW1du1aPfnkk/rjjz+0YcMGMzyVL19eXbt21YgRI8z97ezstGrVKg0cOFABAQEqUqSIQkNDNXbsWLPGx8dHq1ev1uDBgzV9+nSVK1dOn332mYKDg82a7t276+LFixo5cqTi4+NVr149rVmzxmIhkqlTp8rW1lZdu3a1eHA2AAAAADwohRrY5s6dm21b+fLltXXr1lz7qFixon788ccca1q2bKl9+/blWBMeHq7w8PBs252dnTVr1izNmjUr1zEBAAAAQEGwunvYAAAAAAB3ENgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoVamCbPXu26tatK3d3d7m7uysgIEA//fST2X7r1i2FhYWpZMmScnNzU9euXXX+/HmLPuLi4hQSEiJXV1d5eXlpyJAhun37tkXNli1b1KBBAzk5OalKlSpasGBBprHMmjVLlSpVkrOzs5o0aaJdu3ZZtOdlLAAAAABQkAo1sJUrV04TJ05UdHS09uzZo9atW6tjx446fPiwJGnw4MH64YcftHLlSm3dulVnz55Vly5dzP3T0tIUEhKilJQURUZGauHChVqwYIFGjhxp1pw8eVIhISFq1aqVYmJiNGjQIPXt21dr1641a5YvX66IiAiNGjVKe/fulZ+fn4KDg3XhwgWzJrexAAAAAEBBK9TA1qFDB7Vv315Vq1ZVtWrVNH78eLm5uWnHjh1KSEjQ3LlzNWXKFLVu3Vr+/v6aP3++IiMjtWPHDknSunXrdOTIES1evFj16tVTu3btNG7cOM2aNUspKSmSpDlz5sjHx0eTJ0+Wr6+vwsPD1a1bN02dOtUcx5QpU9SvXz/17t1bNWvW1Jw5c+Tq6qp58+ZJUp7GAgAAAAAFzWruYUtLS9OyZcuUlJSkgIAARUdHKzU1VYGBgWZNjRo1VKFCBUVFRUmSoqKiVKdOHZUuXdqsCQ4OVmJionmWLioqyqKPjJqMPlJSUhQdHW1RY2trq8DAQLMmL2PJSnJyshITEy1eAAAAAJBXhR7YDh48KDc3Nzk5OWnAgAH65ptvVLNmTcXHx8vR0VHFihWzqC9durTi4+MlSfHx8RZhLaM9oy2nmsTERN28eVOXLl1SWlpaljV395HbWLIyYcIEeXh4mK/y5cvnbVIAAAAAQFYQ2KpXr66YmBjt3LlTAwcOVGhoqI4cOVLYwyoQw4cPV0JCgvn6448/CntIAAAAAP6L2Bf2ABwdHVWlShVJkr+/v3bv3q3p06ere/fuSklJ0dWrVy3ObJ0/f17e3t6SJG9v70yrOWas3Hh3zb2rOZ4/f17u7u5ycXGRnZ2d7Ozssqy5u4/cxpIVJycnOTk53cdsAAAAAMD/KfQzbPdKT09XcnKy/P395eDgoI0bN5ptsbGxiouLU0BAgCQpICBABw8etFjNcf369XJ3d1fNmjXNmrv7yKjJ6MPR0VH+/v4WNenp6dq4caNZk5exAAAAAEBBK9QzbMOHD1e7du1UoUIFXbt2TUuXLtWWLVu0du1aeXh4qE+fPoqIiFCJEiXk7u6uV199VQEBAWratKkkKSgoSDVr1tSLL76oSZMmKT4+XiNGjFBYWJh5ZmvAgAGaOXOmhg4dqpdeekmbNm3SihUrtHr1anMcERERCg0NVcOGDdW4cWNNmzZNSUlJ6t27tyTlaSwAAAAAUNAKNbBduHBBPXv21Llz5+Th4aG6detq7dq1evLJJyVJU6dOla2trbp27ark5GQFBwfro48+Mve3s7PTqlWrNHDgQAUEBKhIkSIKDQ3V2LFjzRofHx+tXr1agwcP1vTp01WuXDl99tlnCg4ONmu6d++uixcvauTIkYqPj1e9evW0Zs0ai4VIchsLAAAAABS0Qg1sc+fOzbHd2dlZs2bN0qxZs7KtqVixon788ccc+2nZsqX27duXY014eLjCw8P/1lgAAAAAoCBZ3T1sAAAAAIA7CGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUKNbBNmDBBjRo1UtGiReXl5aVOnTopNjbWoqZly5aysbGxeA0YMMCiJi4uTiEhIXJ1dZWXl5eGDBmi27dvW9Rs2bJFDRo0kJOTk6pUqaIFCxZkGs+sWbNUqVIlOTs7q0mTJtq1a5dF+61btxQWFqaSJUvKzc1NXbt21fnz5wtmMgAAAADgHoUa2LZu3aqwsDDt2LFD69evV2pqqoKCgpSUlGRR169fP507d858TZo0yWxLS0tTSEiIUlJSFBkZqYULF2rBggUaOXKkWXPy5EmFhISoVatWiomJ0aBBg9S3b1+tXbvWrFm+fLkiIiI0atQo7d27V35+fgoODtaFCxfMmsGDB+uHH37QypUrtXXrVp09e1ZdunR5gDMEAAAA4GFmX5gHX7NmjcX7BQsWyMvLS9HR0WrRooW53dXVVd7e3ln2sW7dOh05ckQbNmxQ6dKlVa9ePY0bN05vvfWWRo8eLUdHR82ZM0c+Pj6aPHmyJMnX11fbt2/X1KlTFRwcLEmaMmWK+vXrp969e0uS5syZo9WrV2vevHkaNmyYEhISNHfuXC1dulStW7eWJM2fP1++vr7asWOHmjZtWuDzAwAAAODhZlX3sCUkJEiSSpQoYbF9yZIl8vT0VO3atTV8+HDduHHDbIuKilKdOnVUunRpc1twcLASExN1+PBhsyYwMNCiz+DgYEVFRUmSUlJSFB0dbVFja2urwMBAsyY6OlqpqakWNTVq1FCFChXMmnslJycrMTHR4gUAAAAAeVWoZ9julp6erkGDBql58+aqXbu2ub1Hjx6qWLGiypYtqwMHDuitt95SbGysvv76a0lSfHy8RViTZL6Pj4/PsSYxMVE3b97UlStXlJaWlmXNsWPHzD4cHR1VrFixTDUZx7nXhAkTNGbMmPucCQAAAAC4w2oCW1hYmA4dOqTt27dbbO/fv7/5/+vUqaMyZcqoTZs2OnHihCpXrvxPD/O+DB8+XBEREeb7xMRElS9fvhBHBAAAAOC/iVVcEhkeHq5Vq1Zp8+bNKleuXI61TZo0kST99ttvkiRvb+9MKzVmvM+47y27Gnd3d7m4uMjT01N2dnZZ1tzdR0pKiq5evZptzb2cnJzk7u5u8QIAAACAvCrUwGYYhsLDw/XNN99o06ZN8vHxyXWfmJgYSVKZMmUkSQEBATp48KDFao7r16+Xu7u7atasadZs3LjRop/169crICBAkuTo6Ch/f3+LmvT0dG3cuNGs8ff3l4ODg0VNbGys4uLizBoAAAAAKEiFeklkWFiYli5dqu+++05FixY17wXz8PCQi4uLTpw4oaVLl6p9+/YqWbKkDhw4oMGDB6tFixaqW7euJCkoKEg1a9bUiy++qEmTJik+Pl4jRoxQWFiYnJycJEkDBgzQzJkzNXToUL300kvatGmTVqxYodWrV5tjiYiIUGhoqBo2bKjGjRtr2rRpSkpKMleN9PDwUJ8+fRQREaESJUrI3d1dr776qgICAlghEgAAAMADUaiBbfbs2ZLuPBz7bvPnz1evXr3k6OioDRs2mOGpfPny6tq1q0aMGGHW2tnZadWqVRo4cKACAgJUpEgRhYaGauzYsWaNj4+PVq9ercGDB2v69OkqV66cPvvsM3NJf0nq3r27Ll68qJEjRyo+Pl716tXTmjVrLBYimTp1qmxtbdW1a1clJycrODhYH3300QOaHQAAAAAPu0INbIZh5Nhevnx5bd26Ndd+KlasqB9//DHHmpYtW2rfvn051oSHhys8PDzbdmdnZ82aNUuzZs3KdUwAAAAA8HdZxaIjAAAAAIDMCGwAAAAAYKUIbAAAAABgpQhsAAAAAGCl8hXYfv/994IeBwAAAADgHvkKbFWqVFGrVq20ePFi3bp1q6DHBAAAAABQPgPb3r17VbduXUVERMjb21svv/yydu3aVdBjAwAAAICHWr4CW7169TR9+nSdPXtW8+bN07lz5/TYY4+pdu3amjJlii5evFjQ4wQAAACAh87fWnTE3t5eXbp00cqVK/Xee+/pt99+05tvvqny5curZ8+eOnfuXEGNEwAAAAAeOn8rsO3Zs0evvPKKypQpoylTpujNN9/UiRMntH79ep09e1YdO3YsqHECAAAAwEPHPj87TZkyRfPnz1dsbKzat2+vRYsWqX379rK1vZP/fHx8tGDBAlWqVKkgxwoAAAAAD5V8BbbZs2frpZdeUq9evVSmTJksa7y8vDR37ty/NTgAAAAAeJjlK7AdP3481xpHR0eFhobmp3sAAAAAgPJ5D9v8+fO1cuXKTNtXrlyphQsX/u1BAQAAAADyGdgmTJggT0/PTNu9vLz0n//8528PCgAAAACQz8AWFxcnHx+fTNsrVqyouLi4vz0oAAAAAEA+A5uXl5cOHDiQafv+/ftVsmTJvz0oAAAAAEA+A9tzzz2n1157TZs3b1ZaWprS0tK0adMmvf7663r22WcLeowAAAAA8FDK1yqR48aN06lTp9SmTRvZ29/pIj09XT179uQeNgAAAAAoIPkKbI6Ojlq+fLnGjRun/fv3y8XFRXXq1FHFihULenwAAAAA8NDKV2DLUK1aNVWrVq2gxgIAAAAAuEu+AltaWpoWLFigjRs36sKFC0pPT7do37RpU4EMDgAAAAAeZvkKbK+//roWLFigkJAQ1a5dWzY2NgU9LgAAAAB46OUrsC1btkwrVqxQ+/btC3o8AAAAAID/L1/L+js6OqpKlSoFPRYAAAAAwF3yFdjeeOMNTZ8+XYZhFPR4AAAAAAD/X74uidy+fbs2b96sn376SbVq1ZKDg4NF+9dff10ggwMAAACAh1m+AluxYsXUuXPngh4LAAAAAOAu+Qps8+fPL+hxAAAAAADuka972CTp9u3b2rBhgz7++GNdu3ZNknT27Fldv369wAYHAAAAAA+zfJ1hO336tNq2bau4uDglJyfrySefVNGiRfXee+8pOTlZc+bMKehxAgAAAMBDJ19n2F5//XU1bNhQV65ckYuLi7m9c+fO2rhxY4ENDgAAAAAeZvk6w7Zt2zZFRkbK0dHRYnulSpV05syZAhkYAAAAADzs8nWGLT09XWlpaZm2//nnnypatOjfHhQAAAAAIJ+BLSgoSNOmTTPf29jY6Pr16xo1apTat29fUGMDAAAAgIdavi6JnDx5soKDg1WzZk3dunVLPXr00PHjx+Xp6akvvviioMcIAAAAAA+lfAW2cuXKaf/+/Vq2bJkOHDig69evq0+fPnr++ectFiEBAAAAAORfvgKbJNnb2+uFF14oyLEAAAAAAO6Sr8C2aNGiHNt79uyZr8EAAAAAAP5PvgLb66+/bvE+NTVVN27ckKOjo1xdXQlsAAAAAFAA8rVK5JUrVyxe169fV2xsrB577DEWHQEAAACAApKvwJaVqlWrauLEiZnOvgEAAAAA8qfAApt0ZyGSs2fPFmSXAAAAAPDQytc9bN9//73Fe8MwdO7cOc2cOVPNmzcvkIEBAAAAwMMuX2fYOnXqZPHq0qWLRo8erbp162revHl57mfChAlq1KiRihYtKi8vL3Xq1EmxsbEWNbdu3VJYWJhKliwpNzc3de3aVefPn7eoiYuLU0hIiFxdXeXl5aUhQ4bo9u3bFjVbtmxRgwYN5OTkpCpVqmjBggWZxjNr1ixVqlRJzs7OatKkiXbt2nXfYwEAAACAgpKvwJaenm7xSktLU3x8vJYuXaoyZcrkuZ+tW7cqLCxMO3bs0Pr165WamqqgoCAlJSWZNYMHD9YPP/yglStXauvWrTp79qy6dOlitqelpSkkJEQpKSmKjIzUwoULtWDBAo0cOdKsOXnypEJCQtSqVSvFxMRo0KBB6tu3r9auXWvWLF++XBERERo1apT27t0rPz8/BQcH68KFC3keCwAAAAAUpHw/OLsgrFmzxuL9ggUL5OXlpejoaLVo0UIJCQmaO3euli5dqtatW0uS5s+fL19fX+3YsUNNmzbVunXrdOTIEW3YsEGlS5dWvXr1NG7cOL311lsaPXq0HB0dNWfOHPn4+Gjy5MmSJF9fX23fvl1Tp05VcHCwJGnKlCnq16+fevfuLUmaM2eOVq9erXnz5mnYsGF5GgsAAAAAFKR8BbaIiIg8106ZMiXPtQkJCZKkEiVKSJKio6OVmpqqwMBAs6ZGjRqqUKGCoqKi1LRpU0VFRalOnToqXbq0WRMcHKyBAwfq8OHDql+/vqKioiz6yKgZNGiQJCklJUXR0dEaPny42W5ra6vAwEBFRUXleSz3Sk5OVnJysvk+MTExz3MBAAAAAPkKbPv27dO+ffuUmpqq6tWrS5J+/fVX2dnZqUGDBmadjY1NnvtMT0/XoEGD1Lx5c9WuXVuSFB8fL0dHRxUrVsyitnTp0oqPjzdr7g5rGe0ZbTnVJCYm6ubNm7py5YrS0tKyrDl27Fiex3KvCRMmaMyYMXmcAQAAAACwlK/A1qFDBxUtWlQLFy5U8eLFJd15mHbv3r31+OOP64033rjvPsPCwnTo0CFt3749P0OySsOHD7c4G5mYmKjy5csX4ogAAAAA/DfJ16IjkydP1oQJE8ywJknFixfXu+++a94ndj/Cw8O1atUqbd68WeXKlTO3e3t7KyUlRVevXrWoP3/+vLy9vc2ae1dqzHifW427u7tcXFzk6ekpOzu7LGvu7iO3sdzLyclJ7u7uFi8AAAAAyKt8BbbExERdvHgx0/aLFy/q2rVree7HMAyFh4frm2++0aZNm+Tj42PR7u/vLwcHB23cuNHcFhsbq7i4OAUEBEiSAgICdPDgQYvVHNevXy93d3fVrFnTrLm7j4yajD4cHR3l7+9vUZOenq6NGzeaNXkZCwAAAAAUpHxdEtm5c2f17t1bkydPVuPGjSVJO3fu1JAhQ+5rmfuwsDAtXbpU3333nYoWLWreC+bh4SEXFxd5eHioT58+ioiIUIkSJeTu7q5XX31VAQEB5iIfQUFBqlmzpl588UVNmjRJ8fHxGjFihMLCwuTk5CRJGjBggGbOnKmhQ4fqpZde0qZNm7RixQqtXr3aHEtERIRCQ0PVsGFDNW7cWNOmTVNSUpK5amRexgIAAAAABSlfgW3OnDl688031aNHD6Wmpt7pyN5effr00fvvv5/nfmbPni1JatmypcX2+fPnq1evXpKkqVOnytbWVl27dlVycrKCg4P10UcfmbV2dnZatWqVBg4cqICAABUpUkShoaEaO3asWePj46PVq1dr8ODBmj59usqVK6fPPvvMXNJfkrp3766LFy9q5MiRio+PV7169bRmzRqLhUhyGwsAAAAAFCQbwzCM/O6clJSkEydOSJIqV66sIkWKFNjA/hclJibKw8NDCQkJf/t+tr1798rf31/hSzboEV+/AhphwThzdL9mPh+o6Ohoi1VDrQHzBgAAAGuQ12yQr3vYMpw7d07nzp1T1apVVaRIEf2N7AcAAAAAuEe+Attff/2lNm3aqFq1amrfvr3OnTsnSerTp0++lvQHAAAAAGSWr8A2ePBgOTg4KC4uTq6urub27t27a82aNQU2OAAAAAB4mOVr0ZF169Zp7dq1Fs9Mk6SqVavq9OnTBTIwAAAAAHjY5esMW1JSksWZtQyXL182l9IHAAAAAPw9+Qpsjz/+uBYtWmS+t7GxUXp6uiZNmqRWrVoV2OAAAAAA4GGWr0siJ02apDZt2mjPnj1KSUnR0KFDdfjwYV2+fFm//PJLQY8RAAAAAB5K+TrDVrt2bf3666967LHH1LFjRyUlJalLly7at2+fKleuXNBjBAAAAICH0n2fYUtNTVXbtm01Z84cvf322w9iTAAAAAAA5eMMm4ODgw4cOPAgxgIAAAAAuEu+Lol84YUXNHfu3IIeCwAAAADgLvladOT27duaN2+eNmzYIH9/fxUpUsSifcqUKQUyOAAAAAB4mN1XYPv9999VqVIlHTp0SA0aNJAk/frrrxY1NjY2BTc6AAAAAHiI3Vdgq1q1qs6dO6fNmzdLkrp3764PP/xQpUuXfiCDAwAAAICH2X3dw2YYhsX7n376SUlJSQU6IAAAAADAHfladCTDvQEOAAAAAFBw7iuw2djYZLpHjXvWAAAAAODBuK972AzDUK9eveTk5CRJunXrlgYMGJBplcivv/664EYIAAAAAA+p+wpsoaGhFu9feOGFAh0MAAAAAOD/3Fdgmz9//oMaBwAAAADgHn9r0REAAAAAwINDYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArVaiB7eeff1aHDh1UtmxZ2djY6Ntvv7Vo79Wrl2xsbCxebdu2tai5fPmynn/+ebm7u6tYsWLq06ePrl+/blFz4MABPf7443J2dlb58uU1adKkTGNZuXKlatSoIWdnZ9WpU0c//vijRbthGBo5cqTKlCkjFxcXBQYG6vjx4wUzEQAAAACQhUINbElJSfLz89OsWbOyrWnbtq3OnTtnvr744guL9ueff16HDx/W+vXrtWrVKv3888/q37+/2Z6YmKigoCBVrFhR0dHRev/99zV69Gh98sknZk1kZKSee+459enTR/v27VOnTp3UqVMnHTp0yKyZNGmSPvzwQ82ZM0c7d+5UkSJFFBwcrFu3bhXgjAAAAADA/7EvzIO3a9dO7dq1y7HGyclJ3t7eWbYdPXpUa9as0e7du9WwYUNJ0owZM9S+fXt98MEHKlu2rJYsWaKUlBTNmzdPjo6OqlWrlmJiYjRlyhQz2E2fPl1t27bVkCFDJEnjxo3T+vXrNXPmTM2ZM0eGYWjatGkaMWKEOnbsKElatGiRSpcurW+//VbPPvtsQU0JAAAAAJis/h62LVu2yMvLS9WrV9fAgQP1119/mW1RUVEqVqyYGdYkKTAwULa2ttq5c6dZ06JFCzk6Opo1wcHBio2N1ZUrV8yawMBAi+MGBwcrKipKknTy5EnFx8db1Hh4eKhJkyZmTVaSk5OVmJho8QIAAACAvLLqwNa2bVstWrRIGzdu1HvvvaetW7eqXbt2SktLkyTFx8fLy8vLYh97e3uVKFFC8fHxZk3p0qUtajLe51Zzd/vd+2VVk5UJEybIw8PDfJUvX/6+Pj8AAACAh1uhXhKZm7svNaxTp47q1q2rypUra8uWLWrTpk0hjixvhg8froiICPN9YmIioQ0AAABAnln1GbZ7Pfroo/L09NRvv/0mSfL29taFCxcsam7fvq3Lly+b9715e3vr/PnzFjUZ73Orubv97v2yqsmKk5OT3N3dLV4AAAAAkFf/VYHtzz//1F9//aUyZcpIkgICAnT16lVFR0ebNZs2bVJ6erqaNGli1vz8889KTU01a9avX6/q1aurePHiZs3GjRstjrV+/XoFBARIknx8fOTt7W1Rk5iYqJ07d5o1AAAAAFDQCjWwXb9+XTExMYqJiZF0Z3GPmJgYxcXF6fr16xoyZIh27NihU6dOaePGjerYsaOqVKmi4OBgSZKvr6/atm2rfv36adeuXfrll18UHh6uZ599VmXLlpUk9ejRQ46OjurTp48OHz6s5cuXa/r06RaXKr7++utas2aNJk+erGPHjmn06NHas2ePwsPDJUk2NjYaNGiQ3n33XX3//fc6ePCgevbsqbJly6pTp07/6JwBAAAAeHgU6j1se/bsUatWrcz3GSEqNDRUs2fP1oEDB7Rw4UJdvXpVZcuWVVBQkMaNGycnJydznyVLlig8PFxt2rSRra2tunbtqg8//NBs9/Dw0Lp16xQWFiZ/f395enpq5MiRFs9qa9asmZYuXaoRI0bo3//+t6pWrapvv/1WtWvXNmuGDh2qpKQk9e/fX1evXtVjjz2mNWvWyNnZ+UFOEQAAAICHWKEGtpYtW8owjGzb165dm2sfJUqU0NKlS3OsqVu3rrZt25ZjzdNPP62nn34623YbGxuNHTtWY8eOzXVMAAAAAFAQ/qvuYQMAAACAhwmBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsFIENAAAAAKwUgQ0AAAAArBSBDQAAAACsVKEGtp9//lkdOnRQ2bJlZWNjo2+//dai3TAMjRw5UmXKlJGLi4sCAwN1/Phxi5rLly/r+eefl7u7u4oVK6Y+ffro+vXrFjUHDhzQ448/LmdnZ5UvX16TJk3KNJaVK1eqRo0acnZ2Vp06dfTjjz/e91gAAAAAoCAVamBLSkqSn5+fZs2alWX7pEmT9OGHH2rOnDnauXOnihQpouDgYN26dcusef7553X48GGtX79eq1at0s8//6z+/fub7YmJiQoKClLFihUVHR2t999/X6NHj9Ynn3xi1kRGRuq5555Tnz59tG/fPnXq1EmdOnXSoUOH7mssAAAAAFCQ7Avz4O3atVO7du2ybDMMQ9OmTdOIESPUsWNHSdKiRYtUunRpffvtt3r22Wd19OhRrVmzRrt371bDhg0lSTNmzFD79u31wQcfqGzZslqyZIlSUlI0b948OTo6qlatWoqJidGUKVPMYDd9+nS1bdtWQ4YMkSSNGzdO69ev18yZMzVnzpw8jQUAAAAACprV3sN28uRJxcfHKzAw0Nzm4eGhJk2aKCoqSpIUFRWlYsWKmWFNkgIDA2Vra6udO3eaNS1atJCjo6NZExwcrNjYWF25csWsufs4GTUZx8nLWLKSnJysxMREixcAAAAA5JXVBrb4+HhJUunSpS22ly5d2myLj4+Xl5eXRbu9vb1KlChhUZNVH3cfI7uau9tzG0tWJkyYIA8PD/NVvnz5XD41AAAAAPwfqw1s/wuGDx+uhIQE8/XHH38U9pAAAAAA/Bex2sDm7e0tSTp//rzF9vPnz5tt3t7eunDhgkX77du3dfnyZYuarPq4+xjZ1dzdnttYsuLk5CR3d3eLFwAAAADkldUGNh8fH3l7e2vjxo3mtsTERO3cuVMBAQGSpICAAF29elXR0dFmzaZNm5Senq4mTZqYNT///LNSU1PNmvXr16t69eoqXry4WXP3cTJqMo6Tl7EAAAAAQEEr1MB2/fp1xcTEKCYmRtKdxT1iYmIUFxcnGxsbDRo0SO+++66+//57HTx4UD179lTZsmXVqVMnSZKvr6/atm2rfv36adeuXfrll18UHh6uZ599VmXLlpUk9ejRQ46OjurTp48OHz6s5cuXa/r06YqIiDDH8frrr2vNmjWaPHmyjh07ptGjR2vPnj0KDw+XpDyNBQAAAAAKWqEu679nzx61atXKfJ8RokJDQ7VgwQINHTpUSUlJ6t+/v65evarHHntMa9askbOzs7nPkiVLFB4erjZt2sjW1lZdu3bVhx9+aLZ7eHho3bp1CgsLk7+/vzw9PTVy5EiLZ7U1a9ZMS5cu1YgRI/Tvf/9bVatW1bfffqvatWubNXkZCwAAAAAUpEINbC1btpRhGNm229jYaOzYsRo7dmy2NSVKlNDSpUtzPE7dunW1bdu2HGuefvppPf30039rLAAAAABQkKz2HjYAAAAAeNgR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADASll1YBs9erRsbGwsXjVq1DDbb926pbCwMJUsWVJubm7q2rWrzp8/b9FHXFycQkJC5OrqKi8vLw0ZMkS3b9+2qNmyZYsaNGggJycnValSRQsWLMg0llmzZqlSpUpydnZWkyZNtGvXrgfymQEAAAAgg1UHNkmqVauWzp07Z762b99utg0ePFg//PCDVq5cqa1bt+rs2bPq0qWL2Z6WlqaQkBClpKQoMjJSCxcu1IIFCzRy5Eiz5uTJkwoJCVGrVq0UExOjQYMGqW/fvlq7dq1Zs3z5ckVERGjUqFHau3ev/Pz8FBwcrAsXLvwzkwAAAADgoWT1gc3e3l7e3t7my9PTU5KUkJCguXPnasqUKWrdurX8/f01f/58RUZGaseOHZKkdevW6ciRI1q8eLHq1aundu3aady4cZo1a5ZSUlIkSXPmzJGPj48mT54sX19fhYeHq1u3bpo6dao5hilTpqhfv37q3bu3atasqTlz5sjV1VXz5s375ycEAAAAwEPD6gPb8ePHVbZsWT366KN6/vnnFRcXJ0mKjo5WamqqAgMDzdoaNWqoQoUKioqKkiRFRUWpTp06Kl26tFkTHBysxMREHT582Ky5u4+Mmow+UlJSFB0dbVFja2urwMBAsyY7ycnJSkxMtHgBAAAAQF5ZdWBr0qSJFixYoDVr1mj27Nk6efKkHn/8cV27dk3x8fFydHRUsWLFLPYpXbq04uPjJUnx8fEWYS2jPaMtp5rExETdvHlTly5dUlpaWpY1GX1kZ8KECfLw8DBf5cuXv+85AAAAAPDwsi/sAeSkXbt25v+vW7eumjRpoooVK2rFihVycXEpxJHlzfDhwxUREWG+T0xMJLQBAAAAyDOrPsN2r2LFiqlatWr67bff5O3trZSUFF29etWi5vz58/L29pYkeXt7Z1o1MuN9bjXu7u5ycXGRp6en7OzssqzJ6CM7Tk5Ocnd3t3gBAAAAQF79VwW269ev68SJEypTpoz8/f3l4OCgjRs3mu2xsbGKi4tTQECAJCkgIEAHDx60WM1x/fr1cnd3V82aNc2au/vIqMnow9HRUf7+/hY16enp2rhxo1kDAAAAAA+CVQe2N998U1u3btWpU6cUGRmpzp07y87OTs8995w8PDzUp08fRUREaPPmzYqOjlbv3r0VEBCgpk2bSpKCgoJUs2ZNvfjii9q/f7/Wrl2rESNGKCwsTE5OTpKkAQMG6Pfff9fQoUN17NgxffTRR1qxYoUGDx5sjiMiIkKffvqpFi5cqKNHj2rgwIFKSkpS7969C2VeAAAAADwcrPoetj///FPPPfec/vrrL5UqVUqPPfaYduzYoVKlSkmSpk6dKltbW3Xt2lXJyckKDg7WRx99ZO5vZ2enVatWaeDAgQoICFCRIkUUGhqqsWPHmjU+Pj5avXq1Bg8erOnTp6tcuXL67LPPFBwcbNZ0795dFy9e1MiRIxUfH6969eppzZo1mRYiAQAAAICCZNWBbdmyZTm2Ozs7a9asWZo1a1a2NRUrVtSPP/6YYz8tW7bUvn37cqwJDw9XeHh4jjUAAAAAUJCs+pJIAAAAAHiYEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAAAAAwEoR2AAAAADAShHYAAAAAMBKEdgAFLoXX3xR//nPfwp7GFlq2rSpvvrqq8IeBgAAeEgR2ID/YhMmTFCjRo1UtGhReXl5qVOnToqNjbWoadmypWxsbCxeAwYMMNv379+v5557TuXLl5eLi4t8fX01ffr0TMeaNWuWfH195eLiourVq2vRokUW7ampqRo7dqwqV64sZ2dn+fn5ac2aNbl+hv379+vHH3/Ua6+9Zm4bPXq0atSooSJFiqh48eIKDAzUzp07//ZcREREqESJEipfvryWLFli0bZy5Up16NAhU78jRozQsGHDlJ6enutnAQAAKGgENuC/2NatWxUWFqYdO3Zo/fr1Sk1NVVBQkJKSkizq+vXrp3PnzpmvSZMmmW3R0dHy8vLS4sWLdfjwYb399tsaPny4Zs6cadbMnj1bw4cP1+jRo3X48GGNGTNGYWFh+uGHH8yaESNG6OOPP9aMGTN05MgRDRgwQJ07d9a+ffty/AwzZszQ008/LTc3N3NbtWrVNHPmTB08eFDbt29XpUqVFBQUpIsXL+Z7Ln744QctXbpU69at06RJk9S3b19dunRJkpSQkKC3335bs2bNytRvu3btdO3aNf300085fg4AAIAHwb6wBwAg/+49g7VgwQJ5eXkpOjpaLVq0MLe7urrK29s7yz5eeukli/ePPvqooqKi9PXXXys8PFyS9Pnnn+vll19W9+7dzZrdu3frvffeM89Kff7553r77bfVvn17SdLAgQO1YcMGTZ48WYsXL87y2Glpafryyy8zne3q0aOHxfspU6Zo7ty5OnDggNq0aZOvuTh69Khatmyphg0bqmHDhho0aJBOnjwpT09PDR06VAMHDlSFChUy9WtnZ6f27dtr2bJlCgkJyfLYAAAADwpn2ID/IQkJCZKkEiVKWGxfsmSJPD09Vbt2bQ0fPlw3btzItZ+7+0hOTpazs7NFjYuLi3bt2qXU1NQca7Zv357tcQ4cOKCEhAQ1bNgw25qUlBR98skn8vDwkJ+fX47jvvczSP83F35+ftqzZ4+uXLmi6Oho3bx5U1WqVNH27du1d+9ei0sy79W4cWNt27Ytz8cGAAAoKAQ24H9Eenq6Bg0apObNm6t27drm9h49emjx4sXavHmzhg8frs8//1wvvPBCtv1ERkZq+fLl6t+/v7ktODhYn332maKjo2UYhvbs2aPPPvtMqamp5mWFwcHBmjJlio4fP6709HStX79eX3/9tc6dO5ftsU6fPi07Ozt5eXllalu1apXc3Nzk7OysqVOnav369fL09Mz3XAQHB+uFF15Qo0aN1KtXLy1cuFBFihTRwIEDNWfOHM2ePVvVq1dX8+bNdfjwYYv+ypYtqz/++IP72AAAwD+OSyKB/xFhYWE6dOhQpjNadwevOnXqqEyZMmrTpo1OnDihypUrW9QeOnRIHTt21KhRoxQUFGRuf+eddxQfH6+mTZvKMAyVLl1aoaGhmjRpkmxt7/zeZ/r06erXr59q1KghGxsbVa5cWb1799a8efOyHfPNmzfl5OQkGxubTG2tWrVSTEyMLl26pE8//VTPPPOMdu7cmWW4y+tcjB49WqNHjzbfjxkzRoGBgXJwcNC7776rgwcPatWqVerZs6eio6PNOhcXF6Wnpys5OVkuLi65Hh8AAKCgcIYN+B8QHh6uVatWafPmzSpXrlyOtU2aNJEk/fbbbxbbjxw5ojZt2qh///4aMWKERZuLi4vmzZunGzdu6NSpU4qLi1OlSpVUtGhRlSpVSpJUqlQpffvtt0pKStLp06d17Ngxubm56dFHH812LJ6enrpx44ZSUlIytRUpUkRVqlRR06ZNNXfuXNnb22vu3LkFNhfHjh3T4sWLNW7cOG3ZskUtWrRQqVKl9Mwzz2jv3r26du2aWXv58mUVKVKEsAYAAP5xBDbgv5hhGAoPD9c333yjTZs2ycfHJ9d9YmJiJEllypQxtx0+fFitWrVSaGioxo8fn+2+Dg4OKleunOzs7LRs2TL961//Ms+wZXB2dtYjjzyi27dv66uvvlLHjh2z7a9evXqS7oTF3GSc4crO/cyFYRh6+eWXNWXKFLm5uSktLc28Fy/jf9PS0sz6Q4cOqX79+rmOEQAAoKBxSSTwXywsLExLly7Vd999p6JFiyo+Pl6S5OHhIRcXF504cUJLly5V+/btVbJkSR04cECDBw9WixYtVLduXUl3wkjr1q0VHBysiIgIsw87Ozvz7Nmvv/6qXbt2qUmTJrpy5YqmTJmiQ4cOaeHCheZYdu7cqTNnzqhevXo6c+aMRo8erfT0dA0dOjTb8ZcqVUoNGjTQ9u3bzfCWlJSk8ePH66mnnlKZMmV06dIlzZo1S2fOnNHTTz9t7tumTRt17tzZXMkyt7m422effaZSpUqZK1w2b95co0eP1o4dO/TTTz+pZs2aKlasmFm/bds2i0tEAQAA/ikENuC/2OzZsyXdeTj23ebPn69evXrJ0dFRGzZs0LRp05SUlKTy5cura9euFpc8fvnll7p48aIWL15ssfx+xYoVderUKUl3zjZNnjxZsbGxcnBwUKtWrRQZGalKlSqZ9bdu3dKIESP0+++/y83NTe3bt9fnn39uEXyy0rdvXy1atMgMXnZ2djp27JgWLlyoS5cuqWTJkmrUqJG2bdumWrVqmfudOHHCXPAkL3OR4fz58xo/frwiIyPNbY0bN9Ybb7yhkJAQeXl5WQTRM2fOKDIyMttHEwAAADxINoZhGIU9iIdFYmKiPDw8lJCQIHd397/V1969e+Xv76/wJRv0iG/elzr/J5w5ul8znw9UdHS0GjRoUNjDscC8WZ+bN2+qevXqWr58uQICAgp7OJm89dZbunLlij755JPCHgoAAPgfktdswBk2ALmKi4uzOJtV0EaMGKGdO3fKycnpvvf19PTM8oHXBcXLy0sREREPrH8AAICcENgA5CguLk41fH11M5eHbRcWF1dXHTt69IGFtjfeeOOB9AsAAJAXBDYAObp06ZJu3rihZ96dLS+fqoU9HAsXTh7XihEDdenSpQd6lg0AAKCwENgA5ImXT1Wru+8PAADgfx3PYQMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAAAAAK0VgAwAAAAArRWADAAAAACtFYAMAK/fOO++of//+hT2MLD377LOaPHlyYQ8DAID/WQQ2AA+1CRMmqFGjRipatKi8vLzUqVMnxcbGWtR88sknatmypdzd3WVjY6OrV69m6uepp55ShQoV5OzsrDJlyujFF1/U2bNnzfYtW7aoY8eOKlOmjIoUKaJ69eppyZIluY4vPj5e06dP19tvv22xfdasWapUqZKcnZ3VpEkT7dq1K8d+vv76azVs2FDFihUzj//5559b1HzwwQfy8vKSl5dXphC2c+dO+fv76/bt2xbbR4wYofHjxyshISHXzwIAAO4fgQ3AQ23r1q0KCwvTjh07tH79eqWmpiooKEhJSUlmzY0bN9S2bVv9+9//zrafVq1aacWKFYqNjdVXX32lEydOqFu3bmZ7ZGSk6tatq6+++koHDhxQ79691bNnT61atSrH8X322Wdq1qyZKlasaG5bvny5IiIiNGrUKO3du1d+fn4KDg7WhQsXsu2nRIkSevvttxUVFWUev3fv3lq7dq0k6cCBAxo5cqSWLVumL774QiNGjNDBgwclSbdv39aAAQM0Z84c2dvbW/Rbu3ZtVa5cWYsXL87xcwAAgPwhsAF4qK1Zs0a9evVSrVq15OfnpwULFiguLk7R0dFmzaBBgzRs2DA1bdo0234GDx6spk2bqmLFimrWrJmGDRumHTt2KDU1VZL073//W+PGjVOzZs1UuXJlvf7662rbtq2+/vrrHMe3bNkydejQwWLblClT1K9fP/Xu3Vs1a9bUnDlz5Orqqnnz5mXbT8uWLdW5c2f5+vqax69bt662b98uSTp27Jjq1q2r1q1bq02bNqpbt66OHTsmSXr//ffVokULNWrUKMu+O3TooGXLluX4OR60v/76S15eXjp16lShjiMrR44cUbly5Sx+CVAYmKO8adGihZYuXVrYw8gkJSVFlSpV0p49ewp7KJmkpKSoSpUqioyMLOyhZHLp0iV5eXnpzz//LOyhZMJ37f49rN81AhsA3CXj0r4SJUrku4/Lly9ryZIlatasmRwcHHI8Vk7HuXz5so4cOaKGDRua21JSUhQdHa3AwEBzm62trQIDAxUVFZWn8RmGoY0bNyo2NlYtWrSQJNWpU0e//vqr4uLidPr0af3666+qXbu2Tpw4ofnz5+vdd9/Ntr/GjRtrx44dCgkJUdmyZWVjY6Nvv/02y9qjR4/qqaeekoeHh4oUKaJGjRopLi7ObM/L5adZGT9+vDp27KhKlSqZ2+Li4hQSEiJXV1d5eXlpyJAhmS7pvNfly5f1/PPPy93dXcWKFVOfPn10/fp1s/3UqVNq0aKFihQpohYtWmQKP//617/01VdfWWyrWbOmmjZtqilTpuT6OX7++Wd16NAhx3ns1auXbGxsLF5t27bNte//lTnKi7S0NL3zzjvy8fGRi4uLKleurHHjxskwjBz3+/7773X+/Hk9++yz5rZbt24pLCxMJUuWlJubm7p27arz58/n2I9hGBo5cqTKlCkjFxcXBQYG6vjx42Z7cnKyXnzxRbm7u6tatWrasGGDxf7vv/++Xn31VYttjo6OevPNN/XWW2/ldRruW14uEc/KnDlz5OPjo2bNmpnbcvueZCW3ub58+bI6dOggNzc31a9fX/v27bPYPywsLNPl3J6enurZs6dGjRqVlym4b3n5M5uVh/27Jt3/pf3Sw/tdI7ABwP+Xnp6uQYMGqXnz5qpdu/Z97//WW2+pSJEiKlmypOLi4vTdd99lW7tixQrt3r1bvXv3zrYmLi5OhmGobNmy5rZLly4pLS1NpUuXtqgtXbq04uPjcxxfQkKC3Nzc5OjoqJCQEM2YMUNPPvmkJMnX11f/+c9/9OSTTyooKEgTJkyQr6+vXn75ZU2aNElr165V7dq1Vb9+ff38888W/ZYtW1a3b9/Wo48+qlmzZmV7/BMnTuixxx5TjRo1tGXLFh04cEDvvPOOnJ2dzZq8XH56rxs3bmju3Lnq06ePuS0tLU0hISFKSUlRZGSkFi5cqAULFmjkyJE59vX888/r8OHDWr9+vVatWqWff/7ZYsGXN954Q4888ohiYmJUpkwZvfnmm2bb8uXLZWtrq65du2bqt3fv3po9e3auYSgpKUl+fn45zqMktW3bVufOnTNfX3zxRY71/0tzlBfvvfeeZs+erZkzZ+ro0aN67733NGnSJM2YMSPH/T788EP17t1btrb/9+PR4MGD9cMPP2jlypXaunWrzp49qy5duuTYz6RJk/Thhx9qzpw52rlzp4oUKaLg4GDdunVL0p1fTERHRysqKkr9+/dXjx49zDB58uRJffrppxo/fnymfp9//nlt375dhw8fvt8pyZO8XCJ+L8MwNHPmTIvvVsZYc/qeZCW3uR4/fryuXbumvXv3qmXLlurXr5/ZtmPHDu3cuVODBg3K1G/v3r21ZMkSXb58OY8zkXd5/TN7r4f9u5afS/sf5u8agQ0A/r+wsDAdOnQo35f3DRkyRPv27dO6detkZ2ennj17Zvkb/c2bN6t379769NNPVatWrWz7u3nzpiRZBJq/o2jRooqJidHu3bs1fvx4RUREaMuWLWb7gAEDFBsbq9jYWA0YMEALFy5U0aJFFRAQoL59++qbb77RlClT9Oyzzyo5Odncz8XFRZL0yiuvqHPnztke/+2331b79u01adIk1a9fX5UrV9ZTTz0lLy8vsyYvl5/e68cff5STk5PFPuvWrdORI0e0ePFi1atXT+3atdO4ceM0a9YspaSkZNnP0aNHtWbNGn322Wdq0qSJHnvsMc2YMUPLli0zF5A5evSoQkNDVbVqVfXq1UtHjx6VJF29elUjRozI9oe2J598UpcvX9bWrVtz/Czt2rXTu+++m+M8SpKTk5O8vb3NV/HixXOs/1+ao7yIjIxUx44dFRISokqVKqlbt24KCgrK8Tf4Fy9e1KZNmywuQU5ISNDcuXM1ZcoUtW7dWv7+/po/f74iIyO1Y8eOLPsxDEPTpk3TiBEj1LFjR9WtW1eLFi3S2bNnzbMvGWeaa9WqpbCwMF28eFGXLl2SJA0cOFDvvfee3N3dM/VdvHhxNW/e/IFdgpyXS8TvFR0drRMnTigkJMTclpfvyb3yMtdHjx7Vs88+q2rVqql///7mdys1NdW8z9bOzi5T37Vq1VLZsmX1zTff/J3pyVJe/8zeje9a/i7tf5i/awQ2AJAUHh6uVatWafPmzSpXrly++vD09FS1atX05JNPatmyZfrxxx8z/UO7detWdejQQVOnTlXPnj1z7U+Srly5YrHNzs4u02Uy58+fl7e3d4792draqkqVKqpXr57eeOMNdevWTRMmTMiy9tKlSxozZoxmzJihnTt3qlq1aqpatapatWql1NRU/frrr2Ztxm8SS5Uqle2x09PTtXr1alWrVk3BwcHy8vJSkyZN8nz5UE62bdsmf39/i21RUVGqU6eOxZnI4OBgJSYmZvsb46ioKBUrVsziEtTAwEDZ2tpq586dkiQ/Pz9t2LBB6enpWrdunerWrSvpTlgPCwtT+fLls+zb0dFR9erV07Zt2/7WZ82wZcsWeXl5qXr16ho4cKD++uuvHOsftjlq1qyZNm7caH5P9+/fr+3bt6tdu3bZ7rN9+3a5urrK19fX3BYdHa3U1FSLS5Br1KihChUqZHsJ8smTJxUfH2+xj4eHh5o0aWLu4+fnp+3bt+vmzZtau3atypQpI09PTy1ZskTOzs45/vDfuHHjAvse5SYvl4hv27ZN1apVU9GiRc1tefme3Csvc+3n56dNmzbp9u3bWrt2rfndmjRpklq2bGlxvHv9k/OWm4f9u5bfS/sf5u8agQ3AQ80wDIWHh+ubb77Rpk2b5OPjUyD9pqenS5LFmagtW7YoJCRE7733Xp6eq1a5cmW5u7vryJEj5jZHR0f5+/tr48aNFsfauHGjAgIC7nuMd4/vboMHD9bgwYNVrlw5paWlmYunSHdWjUxLSzPfHzp0SOXKlTMDZlYuXLig69eva+LEiWrbtq3WrVunzp07q0uXLn/7jMrp06ctLhuV7jwOIavLRjPashIfH29xtk+S7O3tVaJECXOfDz74QMeOHVOlSpV0/PhxffDBB/r5558VExOjnj176plnntGjjz6qAQMGZDpLVbZsWZ0+ffpvfVbpzuWQixYt0saNG/Xee+9p69atateuncV/k3s9bHM0bNgwPfvss6pRo4YcHBxUv359DRo0SM8//3y2+5w+fVqlS5e2uEQtPj5ejo6OKlasmEVtTpcgZ2zP6bLll156SX5+fqpZs6bGjx+vFStW6MqVKxo5cqRmzJihESNGqEqVKgoODtaZM2cs+imoOcpNXi8Rz+67ldv35F55methw4bJ3t5elStX1jfffKO5c+fq+PHjWrhwod555x0NGDBAjz76qJ555plMjxr5p+YtLx7271p+L+1/mL9r9rmXAMD/rrCwMC1dulTfffedihYtav5l7eHhYV7qFx8fr/j4eP3222+SpIMHD6po0aKqUKGCSpQooZ07d2r37t167LHHVLx4cZ04cULvvPOOKleubIaozZs361//+pdef/11de3a1TyOo6Njtr+9zviN4/bt29WpUydze0REhEJDQ9WwYUM1btxY06ZNU1JSksX9cD179tQjjzxinkGbMGGCGjZsqMqVKys5OVk//vijPv/8c82ePTvTcdevX69ff/1VCxculCQ1atRIx44d008//aQ//vhDdnZ2ql69ulm/bds2BQUF5TjPGQG2Y8eOGjx4sCSpXr16ioyM1Jw5c/TEE0/kuH9Obt68WWCXjebmkUcesXgUQ3JysoKDg7Vw4UK9++67Klq0qGJjY9W2bVt9/PHHFjfzu7i46MaNG397DHcvUlCnTh3VrVtXlStX1pYtW9SmTZss93nY5mjFihVasmSJli5dqlq1aikmJkaDBg1S2bJlFRoamuU+/+QcOTg4ZLo0tHfv3nrttde0b98+ffvtt9q/f78mTZqk1157zWKRloKao9xkXCKesZJsdv7JefPw8Mi0qmLr1q31/vvva8mSJfr9998VGxurfv36aezYsRaLQvxT85YXfNfy52H+rnGGDcBDbfbs2UpISFDLli1VpkwZ87V8+XKzZs6cOapfv75503GLFi1Uv359ff/995IkV1dXff3112rTpo2qV6+uPn36qG7dutq6daucnJwkSQsXLtSNGzc0YcIEi+PkdkN53759tWzZMjPwSFL37t31wQcfaOTIkapXr55iYmK0Zs0ai99WxsXF6dy5c+b7pKQkvfLKK6pVq5aaN2+ur776SosXL1bfvn0tjnfz5k2Fh4fr448/Nn/7W65cOc2YMUO9e/fW+PHjtXDhQjPM3rp1S99++63FDdlZ8fT0lL29vWrWrGmx3dfX12KVyPzw9PS0uGxUkry9vbO8bDSjLSve3t6Zbni/ffu2Ll++nO0+//nPfxQUFCR/f39t2bJFXbt2lYODg7p06WJxf6B059LRnC4bza9HH31Unp6e5i8UsvKwzdGQIUPMs2x16tTRiy++qMGDB2d7CbCU/RylpKRkWq00p0uQM7bfz2XLmzdv1uHDhxUeHq4tW7aoffv2KlKkiJ555pl/7Ht0t/u5RDy7ebvf70l+5nr+/PkqVqyYOnbsqC1btqhTp05ycHDQ008/XSjzllcP+3ctv5f2P8zfNQIbgIeaYRhZvnr16mXWjB49OseaOnXqaNOmTfrrr79069YtnTx5UrNnz9Yjjzxi9rFgwYIs+7j3L/p7tW3bVmXLlrUIkNKdH6hOnz6t5ORk7dy5U02aNLFo37JlixYsWGC+f/fdd3X8+HHdvHlTly9fVmRkpLp3757peC4uLoqNjVW9evUstvft21fx8fE6ffq0xQ3f8+fPV+PGjXNdJMTR0VGNGjXKtET4r7/+avFQ8PyoX7++xWWjkhQQEKCDBw9a/EO+fv16ubu7ZwqNd+9z9epViwUWNm3apPT09EzzK925KX3p0qUaN26cJFlcOpqamprpEsVDhw6pfv36+fuQOfjzzz/1119/qUyZMtnWPGxzdOPGDYvLzSTJzs7O4hcf96pfv77i4+MtfiD09/eXg4ODxSXIsbGxiouLy/YSZB8fH3l7e1vsk5iYqJ07d2a5T8by4h9//LHs7OwK7Xsk5e8S8fr16+vYsWMWCyzd7/dEuv+5vnjxosaOHWuu/FmY83a/HvbvWn4v7X+Yv2sEtvuUn2dGAEB+2djY6JNPPimQpc4fBAcHB82YMUPXr19XTEyMYmJiJN25GT4mJsbi7NmQIUO0fPlyffrpp/rtt980c+ZM/fDDD3rllVfMmvj4eMXExFhcfhoTE5PjEsnBwcE6fPiwxQ8/QUFBqlmzpl588UXt379fa9eu1YgRIxQWFmae9dy1a5dq1Khh3rfh6+urtm3bql+/ftq1a5d++eUXhYeH69lnn81034RhGOrfv7+mTp2qIkWKSJKaN2+uTz/9VEePHtWiRYvUvHlzs/7UqVM6c+aMxU3uWcltHq9fv64hQ4Zox44dOnXqlDZu3KiOHTua96A8DHOUFx06dND48eO1evVqnTp1ylzhNKcFFurXry9PT0/98ssv5jYPDw/16dNHERER2rx5s6Kjo9W7d28FBARY/JKiRo0a5qpwNjY2GjRokN599119//33OnjwoHr27KmyZctaXNqcYdy4cWrfvr35A17z5s319ddf68CBA5o5c6bFHEl5uwQ5v8LCwrR48WItXbrUvEQ8Pj7eXLE2K61atdL169ctFqrJy/fkzJkzqlGjhvlzVF7nOsOgQYPMR0hId+bt888/19GjR/XJJ59YzNuNGzcUHR39QOYtL3/33Yvv2p1L+z/99FMtXLhQR48e1cCBAzNd2n+vh/m7xj1s9yHjmRFz5sxRkyZNNG3aNAUHBys2NjbTDY8AEBcXZy6f/HfVqlVLe/fuLZC+pDuXllSoUOFv95NxSeWWLVvUqlUrc3tERIQkKTQ01DzT17lzZ82ZM0cTJkzQa6+9purVq+urr77SY489Zu43Z84cjRkzxnyf8WDv+fPnW5z1vFudOnXUoEEDrVixQi+//LKkO2dTVq1apYEDByogIEBFihRRaGioxo4da+5348YNxcbGWiyosmTJEoWHh6tNmzbm88I+/PDDTMf85JNPVLp0af3rX/8yt40ePVo9evRQkyZN1LZtW4WFhZltX3zxhYKCgnI9m7hnz54c59HOzk4HDhzQwoULdfXqVZUtW1ZBQUEaN26cGbL+1+coL2bMmKF33nlHr7zyii5cuKCyZcvq5ZdfzvEZc3Z2duYzlO4e89SpU83PmXE/3kcffWSxb2xsrMXCA0OHDlVSUpL69++vq1ev6rHHHtOaNWsy3X9z6NAhrVixwvxhX5K6deumLVu26PHHH1f16tUt7qOJiopSQkKCunXrlt+pyVHGPa0tW7a02J7Tn7+SJUuqc+fOWrJkicUlp7l9T1JTUxUbG2txr09e5lqS1q5dq99++02ff/65uS08PFx79uxRkyZN1LhxY4uHF3/33XeqUKGCHn/88fuaj7zI7c9sVviu3bm0/+LFixo5cqTi4+NVr169TJf23+th/q7ZGFk9JAhZatKkiRo1aqSZM2dKunP6tnz58nr11Vc1bNiwXPdPTEyUh4eHEhISsnzmxf3Yu3ev/P39Fb5kgx7x9ftbfRW0M0f3a+bzgYqOjlaDBg0KezgWmLf7x5zlT1xcnGr4+uqmFd2wfTcXV1cdO3q0QEKbNVi9erWGDBmiQ4cOZboUrrClpKSoatWqWrp0aabfYP+TmKPcxcfHm78cKYjgWNC6d+8uPz+/+3qw/D/hwIEDevLJJ3XixAm5ubkV9nAyadq0qV577TX16NGjsIdi4ruWP/9r37W8ZgPOsOVRxjMjhg8fbm7L7ZkRycnJFktmZ/xGJDEx8W+P5/r165KkM0cPKOVG0t/uryBdPH1C0p0xFsRnLUjM2/1jzvLn1KlTunnjhh7vGaZi3o/kvsM/6Gr8GW1bNEunTp3KtKxxYcu4BOt+FS1aVG3atNHXX3/9wK54yHhI9f06ceKEBg8erDp16jyw72le5u2fmKOs5GXe/ok5uld2cxYREaHvv/9efn6F9wuqrOYsJSVF1apVU58+fQr177vs5u2ll17SypUrVbly5UIY1R1Zzdtff/2l9u3bKyQkpNDmje9a/jwM37WMutzOn3GGLY/Onj2rRx55RJGRkRY3JA4dOlRbt27N8uF8o0ePtri0BwAAAADu9scff+S4Iitn2B6g4cOHm9cyS3cuobx8+bJKliwpGxubQhxZZomJiSpfvrz++OOPv3255sOCOcsf5u3+MWf5w7zdP+Ysf5i3+8ec5Q/zdv+sec4Mw9C1a9cyLdp0LwJbHuXnmRFOTk6ZbgK3tsuP7uXu7m51X2Zrx5zlD/N2/5iz/GHe7h9zlj/M2/1jzvKHebt/1jpnHh4eudZY113HViy/z4wAAAAAgPziDNt9iIiIUGhoqBo2bKjGjRtr2rRpuT4zAgAAAADyi8B2H/LzzIj/Fk5OTho1alSOz/GBJeYsf5i3+8ec5Q/zdv+Ys/xh3u4fc5Y/zNv9+1+YM1aJBAAAAAArxT1sAAAAAGClCGwAAAAAYKUIbAAAAABgpQhsAAAAAGClCGwAAAAAYKUIbEAhSU9PL+whAAAAPLT+WxbL5zlsyFV8fLxiY2Pl4uIib29vVahQobCH9F/n1KlTioqK0vnz59WqVStVqVJFRYoUUXp6umxt+b1JdgzDkI2NTWEPw+r99ddfOnPmjIoUKaISJUqoePHizN194s9i3iQkJCghIUFubm5yc3OTo6Mj37X7xHzlTVbfNdw/vm9Zu3btmgzDUEpKijw9PQt7OLniOWzI0YEDB9ShQwe5urrq8uXL8vDw0JgxY/Tcc88V9tD+axw8eFCtW7eWr6+vDh48qHLlyql69eqaPXu2SpUqxQ+K/9+pU6e0efNmJSYmqn79+nr88cdlY2PD/OTiwIED6tatm2xsbHT58mXVq1dPQ4YMUVBQEP9QZyO771paWprs7OwKe3hW6+DBg3r++eeVmpqqK1euqEOHDurTp4+aNm3Kdy0bf/zxhw4cOKCrV6+qadOmqly5siR+QZCbnL5ryB7/jubNwYMHFR4ervPnz8vNzU2BgYEaO3asdf9SwACycfHiRaNKlSrGm2++aZw/f97YunWr8eabbxo2NjbGxIkTC3t4/xWuX79uPPbYY0Z4eLhx8+ZNIzU11fjkk0+Mxx9/3Khbt64RHx9vGIZhpKWlFfJIC9fBgweNEiVKGK1atTI8PT2NOnXqGMHBwUZycrJhGMxPds6dO2eUL1/eiIiIMGJjY40vvvjCePHFFw0HBwdjyZIlhmEYRnp6eiGP0rrk9l27fft2IY/QOv3xxx9G6dKljddff92IiooyJk+ebLRr187w8fExfvrpJ8Mw+K7d68CBA4aXl5fRpEkTw8HBwWjQoIHx8ssvm3+f8fda1vLyXUNm/DuaNydPnjQ8PT2NN954w5g3b57x/vvvG8WLFzcCAwONI0eOFPbwskVgQ7Z+++03o1atWsbBgwfNbWlpacbMmTMNW1tbY8aMGYZh8I90Ti5evGjUqFHD+Oqrr8xtqampxqZNm4zmzZsbzZo1My5fvlyIIyx8SUlJxuOPP24MGDDASEtLM65cuWKsXLnSqF27tlGrVi3j2rVrhmHwg3RW9uzZY/j5+Rl//vmnue3cuXPG0KFDDRsbG+PLL78sxNFZn7x+11JTUwt5pNZnzZo1RuPGjY3ExERz2969e41evXoZpUqVMjZs2FCIo7M+CQkJRoMGDYzXX3/dSExMNC5cuGBMnDjR8PPzMwIDAwltOeC7dv/4dzTvFi1aZDRo0MBISkoyt/32229GhQoVjMcee8w4deqUYRjW97Mt50eRrVu3bunIkSO6fPmypDvXQdva2iosLEzvvfeeBg8erM2bN3MZTA7c3d1VrFgxRUZGmtvs7e3VsmVL/fvf/9bt27c1Y8aMQhxh4UtJSVFCQoICAwNla2urYsWKqVOnTvr8889lZ2enJ554QoZhyM7O7r/m5uB/ys2bN3XgwAFdvHjR3Obt7a1hw4bp9ddfV1hYmKKjowtxhNYlr981e3t7vmv3uHnzpmJiYhQfH29uq1+/voYOHaq2bdvq7bff1vHjxwtxhNYlMTFR169fV5cuXVS0aFGVKlVKr776qkaPHq1z586pS5cu5r+psMR37f7x72jeXbp0SdeuXZOrq6ukO3NXuXJl7dixQydPntSQIUMkyep+tuVvCmSS8Ye5Vq1a6tq1q9599139/vvvsrGxkXHnrKz69OmjkJAQffPNN0pPT3/o/wLIjp2dnZo1a6Zt27b9v/buPKDGtP0D+Pe0nfZkS0SFqJQWWSoVKkuyiywT79vIII09xk7GOpaxjG0GU++Ln7EMxjooFcZaqaiEsjQyHamk7XT9/mjOMw5mBu+M54xzff4Z51nq6pnrWe7z3Pd1Iz4+XlgukUgQEBAAZ2dnnDhxQsQIxWdiYgJNTU38+OOPwjItLS04Oztj48aNKCsrU9kLqBjy8/OFf7dq1QpdunTBxo0blR5uTE1NERYWhhYtWiAxMRHAP6cS1t+Jc+3dOTg4wMnJCfv370dJSYmw3M7ODiNGjEBJSQnS09NFjFC1GBsbA4Bw/gGAvr4+AgICMHPmTNy9exfr168XKzyVpLhG2dnZca69Jb62vTl/f3/k5uZi8+bNAAAdHR1UVlbC3Nwcu3fvxvHjx7Fv3z6Ro3wVN9iYoLi4GMXFxUoPhEFBQSgqKsLq1atx7949SCQSSCQSmJqaonbt2rh58yY0NDTU/gKg8PPPPyMuLg4XL17E3bt3oampiZkzZ6K0tBTTpk3DlStXIJfLhe19fX3x5MkTPHnyRMSoxVVdXY3+/fsjKSkJhw4dUlrXvn17BAQE4MqVKygvLxcpQtWRnp6OBg0aYO7cuQBqGmY9evRAfHw8du7cKbwNB2oebgwMDHD27FkA6nuTfvLkCYqKigDUHIMBAwZwrr2B69evIyYmRrheNW/eHJ6enli7di1OnjypdIx8fX2hq6uLI0eOiBWuSqmsrISuri68vLxw+vRpJCUlCet0dHTQv39/NGvWDKdPnxYvSBVSUlKCoqIi4dmjZcuW6NChA+faWyAivrb9AcV1jIjQrFkzjBs3Dps3b8aBAwcAANra2gBq7psWFha4f/++WKH+Lm6wMQA1N+fAwED4+PggICAAY8eORXFxMQYNGoRBgwYhPj4eCxcuxK1bt4R9tLS00KhRI1RWVooYuepISUlB+/bt8cknn6BXr17o2rUroqOjUbt2bZw5cwaPHj1CREQE9u3bh8rKShAR4uPjUadOHUilUrHDf2/u3r2LzZs3Y9GiRUhJSYGmpiaGDRsGLS0trFu3TukbQg0NDbRp0wYPHjxAcXGxiFGLLykpCR4eHqhfvz4uXLiABw8eAAAmT54MX19fbNiwARs3bkReXp6wT+3atWFlZaW2c/6lpqYiKCgI3333HZ4/fw4ACAkJ4Vz7E8nJyXByckJWVhY0NTWF/Fm1ahXc3NwwevRo7N27F0+fPhX2adiwIaytrcUKWXSpqamIjIwEUPPwp6Ojg2nTpuHGjRuYP38+MjMzhW2lUik6d+6MO3fu4NmzZ2KFrBLS0tIQFBQET09PDBw4EJs2bQIAfPnll2jbti3n2u8oKChAcnIyUlJSUFBQAA0NDb6PvkZubi4KCwuhqamJqqoqSCQS6OnpYdiwYbC2tsby5cuxe/duYfvatWsL1bsBFeuZ8r4HzTHVk5mZSfXr16cZM2bQ/v376T//+Q/VqlWL/Pz86PLly0REtH79evLx8SEzMzMaPnw49enTh4yMjCglJUXk6FXDH1XUXLBggbCNr68vOTs7k7m5Ofn7+1OtWrXo2rVr4gb/HqWkpFDjxo3J3d2dbG1tSV9fX8ihmzdvkpOTE/n7+9OWLVuIiKi8vJwmTpxIXl5eVFJSImbookpKSiI9PT1auHAhJSQkkJaWFh05ckRpm2nTppGrqyu5ubnRxIkTadiwYWRkZESpqakiRS2utLQ0MjExoU8//VSoxvriOmdnZ/Lz8+Nce0lycjLp6enR9OnTlZa/OAB/8ODB1Lx5cwoKCqJFixbR6NGjycTEhG7cuPG+w1UJ5eXl1Lx5c5JIJBQSEqK0LikpiUxMTKhXr1504sQJYfno0aMpICCAysrK3ne4KiM1NZVq1apFEydOpE2bNlFYWBj5+PhQfn6+sE1wcDDn2ktSUlLIxcWFWrZsSRYWFhQYGEhZWVlEVHMfdXZ25vso1RwLbW1tsrS0FHKqoqJCWH/+/Hn66KOPyMLCgqZMmULR0dEUERFBJiYmwvFUJdxgY7Rw4UIKDg5WWjZhwgSSSCTk4eFBd+7cISKiS5cu0YoVK2jQoEE0YcIESktLEyFa1fRnFTVXrlxJRERFRUUUFxdHn3/+OW3dulUlLwp/l/v375OVlRXNmzePSktL6d69e9S+fXvat2+fsE1mZiYFBQVRixYtyNzcnDp16kSmpqZq1ah92fXr10kikdDMmTOFZUFBQeTl5fVKhdFDhw7RtGnTqEuXLjRy5Ei1/UKlrKyMhg4dSmPHjiWimsZGQkICHTp0SCjbnJ2dTUFBQdSyZUvOtV9lZ2eTRCKhiRMnElHNcVu1ahX9+9//psjISIqJiRG2XbduHY0YMYJcXV2pX79+lJSUJFbYKqF///4UERFBtra2NHjwYKV1N27cIDc3N3JxcSF7e3vq1asXGRsbq/Uxu3//PtnZ2dGMGTOEZceOHaMePXrQw4cPKTs7W1jOufabjIwMql+/Pk2dOpXS0tIoOjqafH19aenSpULF0aysLLW/tv3888/k6+tLvr6+5OnpSXZ2dvTo0SMiUm60ZWdn06ZNm6hp06bk5uZGHTt2VNn84omzGYKDg1FSUoLDhw8Lkytu2rQJN2/exK5du+Dh4YG9e/eKHaZKS0tLg6OjI2JjY+Ht7a00ieyKFSswY8YMHD16FH5+fiJHKp7jx49j3rx5OHr0KGrVqgUA6NGjB1xcXFBaWorOnTujT58+KCwsxN27d3H06FE0atQIHh4eaN68ubjBi0Qul2PNmjUoLS3FrFmzhOVbtmxBVFQUvv/+ezg7O6OiokJpws+qqipoaGiobQU6IoKXlxcmTJiA/v37o3v37sjLy4NMJsOTJ08QFRWFSZMmQSaTITc3F8eOHUPDhg3VOtcA4MqVK2jXrh3GjRuHhQsXolevXqisrEStWrVQVlaGjIwMfPzxx1iwYIGwz/Pnz6GpqanaE86+B+Hh4TAxMYGrqyvGjx+PLl26ICYmBnv27EHv3r0hk8lw+fJlnDlzBhYWFujZsydatmwpdtiiuXr1Knbs2IGIiAhhMvHZs2dj69at0NXVhVQqhbe3t1AYAuBce/bsGcLCwqCnp4etW7cKyz/++GPcuHEDiYmJwrPHkydPkJubiyNHjqjlffTUqVPYuHEjxo4dC11dXURGRuKXX35BbGws6tev/8o9UzG2r6qqCgYGBmKF/cdEbS4ylbB582ZycHAQJqTMysoiPT092r17N505c4bq1atHV69eFTlK1Tdw4EDy9/cXvhmsrq6m6upqkslk1KdPHxo/fjxVVVWp7Twoe/bsIYlEQpmZmUREtHTpUtLW1qZ+/fpR7969SSKR0Jo1a0SOUvUo5s8hUu6W5uDgQEFBQWKEpNLkcjnJZDJydXWlo0eP0sqVK6l79+6UmZlJ9+/fp1WrVpFEIlF6W8R+k5CQQMbGxqSjo0MDBw6k3NxcIiLKz8+nzz//nKytren8+fMiR6k6FNfzpUuX0uTJk4mIaO/evWRpaUkNGzYkKysr+uWXX8QMUSWVl5crzR+5evVqMjY2pm+//ZZOnTpFu3fvJqlUSps2bRIxStVSUFBAkyZNEq5ditw7fPgwdejQgSoqKqiqqkrl5g8TS1xcnPDvhIQE8vLyIjs7O6GLvGK+zRffuKkybrAxSklJoSFDhpCZmRm5u7uTnp6e0JXo3r17VKtWLTp58qTIUaqmFxtfu3fvpg4dOtD48eOFhxyFf/3rX+Tv7/++w1MpT58+pR49epBEIqHAwEDS0NCgo0ePCsdwyZIlZGJiQnl5eSJHqtoUx2vTpk1ka2tLly5dEjki1TRmzBiys7OjHj16CGM5FKZOnUoODg5UXFzMExe/RkJCAnXt2vWV635qairp6+vT3r17RYpMdZ07d07pGu/l5UVSqVRp2T/lwVAMmzdvpjNnzgifi4qKyMnJiebOnStaTKpIUVeA6Lcv8I4fP06tW7emsrIyYVlBQYEo8amC37umJyYmUseOHZW6Ry5atIgSEhLeZ3jvTD37yzAljo6OWLhwITZs2ID+/fsjJiZGmB+mrKwMzZo1Q926dUWOUnVkZmZi+fLlAGrmWVNUyRw0aBD69euHxMRErqj5GsbGxtizZw+OHz+O4cOHIzAwEP7+/kI1JhsbGzRu3BhaWloiR6qa6Nfe65qamgCALl264PHjx0rVwNhvQkJCYGZmhhMnTghVWCsqKgAATZs2hYmJCfT19dW22+jvISJ4enpi27Zt8PT0FJYBgKGhIezs7GBmZiZmiCpJW1tbKAUeFhaGrKwszJs3D9nZ2QgMDBS2YcoU1/9Ro0ahU6dOQq5JJBLUq1dPqAZJPHoHANCmTRsAUBp2UVJSgpKSEmhqakIikWDWrFno1q0bKioq1PK4vXxNVxwDd3d3LFu2DHXr1oWfnx9CQ0Mxa9Ys1KlTR4ww3xo/Gam5yspKaGtrw8rKSuhH/qJNmzahpKQEDRs2FCE61XPr1i14e3vjyZMnkMlkWLx4MbS1tYX+0NOmTYOenh727duHjh07wt/fH8XFxTh9+jQSExPV/oZtYGAAf39/xMTEID09XZivCAB++uknmJqaqv0xeplcLhdKEmtraws3n+bNmyMyMhJffPEF+vbtC1tbW5EjVS0dOnTA0KFDkZGRgcjISHh6eqJp06YAaqaWUIzL0tPTU9s56l5WVVUFLS0tVFVVKV3zFcdn06ZNeP78+WvvFepK8eDs5uYGGxsbuLu7Izc3FydPnoStrS0aNmyI5cuX48GDB2jUqJHY4aoMxXVNMW5e8V9Fri1ZsgS3b99Gp06dAKjvPJIve/E4KY6hsbExdHV1oaWlhdmzZ2PlypU4e/as2o71A367lgFQOlbu7u6IiopCQEAAHjx4gKtXr/5z7p1ivdpj4lP0371z545SNUgiolOnTtGgQYOoTp06alVZ6I/IZDLq378/9e3bl2bOnEn29vY0ZcoUYX15ebnw76tXr3JFzRcouvE9efKEiGqqQbq7u1NgYCCtWbOGRo8eTbVr16bk5GQRo1Q9inP07t275OHhQXfv3lVaf/jwYXJ1deVupC9Q5JqiXPqePXvI1dWVDA0NqU+fPhQQEEC1atVS2wqaL1N0H1LkWk5ODg0aNIgePnwobHPp0iUaM2YMmZqaqmwFNTG8fF3r3bs3WVhYKI35Li0tpadPn4oRnsp5k1y7cuUKjRs3jmrXrs3PHi9R5NvLYyLj4+OpY8eO9Omnn5KOjo5St8kPnVwuf6UugOLaf/v2bVq3bt0r+4wdO5Z0dXX/cVPecF8QNXDv3j2cOHECMTExePLkidAtSEtLCzk5OejYsSMcHBxgaWkp7OPq6oratWsjLi4Ozs7OIkWuWnR0dFC/fn2EhIRg4sSJCAkJwZEjRzB16lRhvaLLo4uLCyZPnozdu3dj1apVsLe3FzN0UVVWVkJTUxM5OTnw8vLCDz/8ABsbG4wePRpyuRxfffUV8vLyEBsbi9atW4sdrij+7Bz19PREq1at0KRJE6X9evbsiePHj6NBgwZihK1yXsw1FxcXnDhxAgMHDsT+/fsxf/581K9fH05OTjh//jwcHR3FDlcUeXl5uHjxIo4fPw65XC50H9LS0sLdu3fh7u6OevXqCTn18OFDHDt2DBkZGYiNjYWTk5OY4auMF3PN09MTZ8+exffff4+zZ8/CxcVF2E5PTw/GxsYiRiqet821vLw8nDp1Cjk5OYiNjeVnjxdUVFQI+ebo6Ijo6GhhXUFBARITE7F161acP39e6Db5oUtPT0dISAi6deuGMWPG4IcffoBcLodUKkV2djbc3d1x4cIFpX0uXLiAy5cvIzExEa1atRIp8nfDZf0/cCkpKejWrRvq1auHnJwc1KpVC2FhYQgJCUHjxo3h5eUFBwcHbNiwQehyoHjlTi/0kVZ3imNRWloKfX19AEB+fj6++eYbREdHIyAgQBjXVl5eDk1NTbUbi3Xr1i189913ePr0KVq3bo1evXrB0NAQAHD79m14e3sjMDAQ69atE45NRUUFnj17Bl1dXejp6YkZvmje5RwFfjtP1dG75BqrybXevXtDKpXi0aNHMDc3x5w5c+Dv74+6deuiefPm8PPzw1dffaWUa3l5edDV1YWpqamI0YvjTXNt7dq13J37Be+aa/n5+ZBKpTAxMRExevFkZmbi66+/Rn5+PpydnREQEAAbGxsANV25PT090bt3b6X7QWpqKiIjI7F8+XK1+XI4IyMD7du3R48ePWBlZYWjR49CW1sbPj4+mD59Onr16gV7e3ts3br1ledYmUyG2rVrixT5/0DcF3zs76QobT1t2jR69OgRyeVymjx5MrVv355GjBhBT548odu3b6ttmfm39WKpXMUxe/z4MX3++edK3SPDwsKEiWfVRWpqKtWqVYt8fHzI29ubtLS0aMCAAXTkyBEiqqnYFxoaKhxDLjtcg8/Rt/e2uUbE+UZUU5bf1taWPvvsM8rOzqYHDx7Q4MGDyc7OjubNm0dVVVV07do1pQpr6n7c3iXXGOfau0pLSyMTExPq3r07DRgwgExMTMjPz482b95MRDUVDSMiIl57rAoLC993uKKprq6mzz77jAYNGiQsKyoqoqioKHJ1daWuXbvSjh07Xtnvn14RmBtsH7CcnByytLSkH3/8UWn52rVrqV27djRu3DieH+Z/oDj5Hz9+TIsXLyZHR0eytbUlbW1t+umnn0SO7v0pLS2lwMBAGjdunLDsypUr5ObmRr6+vnTq1CkRo1NtfI6+Hc61d5eWlkZWVlavjG+JjIwke3t7Wr58OT179kyk6FQP59q741x7e+Xl5TR8+HAaNWqUsCwrK4sGDx5MHTp0oK+//vq1+6lrQ3fkyJHk7e2ttKyoqIiWLVtGXl5etGTJEpEi+/uoZ38aNaGhoQF9fX08fPgQQE3VHAAIDw9HUFAQTp8+jYSEBAC/ldZlv08ulwOAME5N8Zq9bt26CAkJgYaGBvLz83H16lW0a9dOtDjfNz09PRQUFAhTP1RXV8PV1RXR0dGQy+VYsmQJkpOTRY5SNfE5+nb09PQgk8k4195BeXk5qqqqUFpaCgB4/vw5gJpqfH5+ftiwYYMwFQnnGufa/6KyspJz7S3p6Ojg0aNHwnMFEaF58+ZYtmwZbG1t8c033+Dw4cOv7Kduw1bo11Fcrq6ukMvlyMjIENYZGRnh448/hq2tLQ4ePIji4mKxwvxbcIPtA2ZhYYFmzZph1apVePr0qVCuGQCmTJkCKysrrFmzBsCr81YwZVVVVcKA306dOiEnJ0e4UFZWVmLt2rW4efMmzpw5AwcHB5GjfT/u37+Py5cvQy6XQ1dXF/n5+QBqLqhVVVWwtbXF+vXrcePGDWzbtk3kaFVHaWmpUFTEwsICNjY2fI7+CcVDXXFxMaRSKefaG8rLy0N6ejqAmkJIDRo0wNy5cwHUNEjKy8sBAGvWrEGdOnWwePFiAOqdawolJSXC+CuAc+1tODk5wdzcnHPtDcnlclRWVsLCwgIymUw4VtXV1WjSpAlmz56N6upqxMTEiByp+BTPXQEBAcjIyMCyZctQUlICoOYcNTU1xezZs3H+/HkkJiaKGepfT8zXe+yvde/ePdq9ezft3btXKCv8+PFjsra2Jn9/f6Wy80REq1evJi8vLx4f84Lc3Fw6fvw4RUdHk0wmUzpmd+/epUaNGtGoUaOUuiGUlpZSaGioWpUgTk1NpcaNGwtj9Xbu3EkSiYT27t1LRDXdRSsqKoiI6L///S+ZmppSTk6OaPGqiuvXr1PPnj0pLi6OSkpKiIjP0T9z7do1CgwMFI7Xnj17ONfewP3796lOnTrUr18/On/+PBHVHMu6devSkCFDhO0UJdYnTZpEvXr1EiVWVVFQUEA3btygjIwMIiI6dOgQ59obKCkpoaKiIqXpC65evUr169fnXPsDL1/XY2NjSVNTk9asWfPKNrGxsaShofGPK0X/dzp9+jRJpVIaN24cPX78WFiel5dHTk5OdO7cORGj++txg+0DkZKSQpaWluTm5kZmZmbUq1cv4aZz/vx5srCwIB8fH8rMzKTnz58TEVFoaCgFBAS88pCorpKTk6lBgwbk6OhIxsbG1KRJE4qKiqLc3FwiIurYsSN98sknry1m8E8fzPo2kpKSSF9fn6ytrcnMzEyYA2z8+PEklUrp8OHDStsfOXKE7Ozs1H4slqKAwejRo4WcUjh//jw1bNiQz9GXJCUlkZ6eHkVGRgrLKisrKTw8nKRSKR08eFBpe86135w5c4a0tLSoS5cuFBISInyJt2vXLjI1NaW+fftSRUWFcO0aPnw4BQcHU2VlpVqOi7l+/Tq5uLiQo6MjaWtr0/z586msrEy4rh06dEhpe861GmlpadS1a1dycXGhhg0bUkxMDBERPX/+nHbu3El169algQMHcq69JCMjg1asWKE0Bx0R0YoVK0hDQ4O2bNmitPzKlStkZ2enNF8uIzp48CBJpVLq378/7dq1i9LT02n69Olkbm5O9+7dEzu8vxQ32D4Aijc/06dPp5KSEjpy5Ag1aNBAqfBFamoq2dvbk42NDbVr14769OlDhoaGPFHxr7ha35tRPEB/9tln9PjxY7K3t6eoqCgiqpmkMiwsjLS1tWnTpk2Ul5dHz58/p+nTp5OTkxPJZDKRoxdPSUkJde3alcaMGSMsu3HjBl27dk24qfA5qiw5OZkMDAxo6tSpSsurqqrol19+oXHjxpG2tjZ99dVXnGuvUVBQQL1796ZNmzaRq6srDR06lDIzM4mI6MCBA2Rvb08tW7akvn370qBBg8jAwICuX78uctTiSEtLozp16tCUKVMoLS2NVqxYQRKJhB48eEAPHjygUaNGca69huK4TZw4kf7zn//QpEmTSFtbW/hy4NmzZ3Tw4EGysLAgW1tbzrVfZWVlUe3atUkikdCMGTOU3g49e/aM5s+fTxKJhGbNmkVXr16lgoICmj59OjVv3pzy8/NFjFw1XblyhXx8fMjS0pKaNWtGLVq0UJq8/kPB87B9ADZv3oydO3fi9OnTQv/enj17ok+fPpBKpbC0tESnTp0AAGvXrsXDhw8hlUoxZMgQtGzZUsTIVUdubi68vb3x9ddfw9fXV1i+bt06REdHo23btpg/fz7q1KkjYpTiSklJQbt27TB58mQsWrQI1dXVGDx4MO7cuYPLly8DqBkzs337dsybNw8WFhYwNDREXl4ejh8/rjSZrLopLy+Hn58fvvzyS7Ru3Ro9e/aETCbDjRs30KpVK4waNQqhoaEA+BwFgJ9//hkuLi5wcnLCsWPHIJfLMWXKFGRkZCAnJwdjxoyBg4MDrl+/jilTpqBRo0YwMjLiXPuVXC6HTCZDx44dcfr0aVy8eBGLFy9G69atcevWLZiZmWHr1q1YsGABCgsLoaurizFjxqjNHE4v+uWXXzBgwAC4uLhg9erVAGrGwvTo0QPz58+Hvr4+ysrKcPnyZUyYMIFz7VcymQxDhgyBra2tMM4WADp37gxHR0d8+eWXwrLi4mJERUVBJpOpda4BwLNnzxAREYHq6mq0bdsW4eHhmDJlCqZOnYp69eoBgDBeLTIyEpqamjAyMkJRUREOHToEV1dXkf8C1VRUVASZTIbi4mKYm5sLxYI+JDyj6AeAiJCbm4ukpCS4uLhg0aJFOHr0KCoqKlBYWIjc3FxERUVh1KhRGD9+vNjhqiSJRAI9PT2lan1aWloIDw9HWVkZvvnmG/j7+6NPnz5qO2FxeXk5pk2bhgULFgjHICoqCu3bt8f69esxbtw4mJubY8aMGejZsydu3rwJIkKHDh1gaWkpdviiKiwsREZGBn755RdMnToVALB161Y8fPgQp0+fxqxZs6Cvr48hQ4bwOford3d33Lt3D99//z02btyIyspKODs7w9raGqtXr0bnzp2xevVq+Pj4cK69RENDA/Xq1UPbtm2RmpqKfv36QSqVYsSIESgrK8Pq1athZGSE5cuXA1DvSdglEgm6d++OgQMHCsuioqJw4sQJ5OXlobCwEPb29li5ciVSUlKQnJzMuYaaYluFhYXCcVPkkLW1NWQyGYCaZxMigpGREZYuXaq0nbrS0NBAmzZtUKdOHQwePBh169ZFcHAwAAiNNg0NDYSEhMDb2xu5ubkoLS2Fo6MjGjVqJHL0qsvY2BjGxsZih/H3Eu3dHvvL3L59mzw8PKh58+Y0YMAAkkgkdODAAaqurqZHjx5RREQEderUiR4/fiz0IVfXfuMvksvlSmPPgoKCyNHRUZiAUjFAmoioR48e1Llz5/ceoyqrrq6mwsJCoZtLZWXlK8eU1aiurqbg4GAKDw+nwMBAOnbsmLDu3r17NHz4cPrkk0+EY6jYR509fPiQQkJCSE9Pj/z9/ZXGCsXExJCJickr44qYspCQEJo+fToR1YyHNDU1JXt7e/r3v/8tFCIh4lwrKioS/q0ooLR7924qKCig2NhYcnNzozlz5ogYoWpSdLElIqEYy6xZs+ijjz5S2u7FYiTqnmtEJBRQUti1axdJJBKaMmWK0D2ysrKSC9owJfyG7QNgbW2NmJgYXLp0Cenp6ZBIJOjTpw8AoH79+mjYsCHi4uJgaGgofLOlbnN3vCw9PR1LlizB/fv3YWVlhaCgIKxfvx7+/v7o168fjh07Bh0dHWH7bt26Ye/evZDL5dDU1BQxctUhkUhgYmKCjz76CAMHDkRERAQ8PT3FDkslSSQSTJ48GZ06dUJpaSnCwsKEdRYWFjAzM8OlS5egqakpnJvqfo6am5tj8eLFaNSoEfz8/FCnTh0QESQSCYYNG4Z58+YhLi4OgYGBYoeqchTHqUuXLrhz5w7Gjh2LI0eO4MqVK0hKSsLUqVOho6MDFxcXSKVStc81IyMj4d/u7u64fPmy0PXMx8cHZmZmuHr1qljhqSwbGxsANW/NtLW1AdTknmLaDQBYvHgxpFIpIiIioKWlpfa5BgAGBgYAaroua2hoYPDgwSAiDB06FBKJBBMmTMCKFSuQk5ODb7/9Fvr6+nzcGHeJ/FBYW1vD2toaW7duxeXLl1FRUSE0OB49egQrKyth4md1d/PmTXTs2BH9+/dHYGAgTpw4gfDwcPTq1QsbNmxAWFgYunTpgm3btqFx48bQ1dXF9evXYWRkxA221wgMDIS/vz+++uoruLq6Qk9PT+yQVJKbmxuOHj0KHx8fbN68GU2bNkWrVq0A1HQvatGiBaqqqoQHHwY0bNgQ06dPh66uLoCaRiwRQSaToV69emo7fujPKB7urK2t8a9//QtmZmY4fPiwcJ+QSCRwcnKCVCoVOVLVY2lpKXR1rK6uRkVFBQwNDdG6dWuRI1NdGhoawpcEis8AMGfOHERFReHatWvQ0uLHzZdpamqCiFBdXY3g4GBIJBJ89NFHOHjwILKzs3Hp0iWhcccYFx35wKSnp8PDwwMzZ85EgwYNkJqais2bN+Ps2bNwdHQUOzzRlZeXIzQ0FHXq1BEGSpeVlcHd3R3JyckIDg7GjBkzMGrUKDx+/Bh169aFubk5Tp06hcTERL5p/44lS5Zg8eLFyMjIQIMGDcQOR6WdPXsWQ4YMgYWFBRwdHVFRUYGDBw8iISFBbSZd/1/NnTsXO3fuxMmTJ9V6HNGfqaysRHR0NNzc3NC6dWulh2r2ZubMmYMdO3bgxx9/FN4osVcpxqbNmzcPeXl5sLGxwaxZs3Du3DkulPEnFI/hEokEvr6+SEpKQmxsLD+zMSX8lccHxt7eHvv378eoUaOgoaGBRo0aIS4ujk/8X0mlUvz888/CjbesrAy6urro2rUrmjZtiszMTMTHx+PChQtYt24dHjx4AKlUiqVLl6pltb4/o3gAHD16NL777juUlZWJHZLK8/b2xunTpxETE4MLFy7AxsaGG2tvaNeuXThz5gz27NmDU6dOcWPtT2hra2PkyJHcFf4d7NmzB3Fxcdi1axdOnjzJjbU/ocgxbW1tbNmyBcbGxkhISODG2huQSCSQy+WYOnUqzpw5g6SkJH5mY6/gN2wfKJlMhsrKSkilUtSqVUvscFQCEeH58+fo3r07rKys8M0330BLSwsPHjyAp6cn5s6di9OnTyMnJwdnz54VO9x/FCJCaWkpd994S9XV1QCg1lXT3kZKSgo+++wzLF26VOhOytjfIS0tDQsWLMC8efNgZ2cndjj/GJcvX0a7du2QmpqqtqX734VcLsf27dvRpk0bODs7ix0OU0HcYGNqJzExEd7e3ujYsSMsLS2xb98+DBkyBFu2bEFqaio8PDxw8eJF2NjYCH3M+ZtpxlTDi+NzGfs7VVZW8pjSd/Ds2TP+8u4d8LMG+yP8tS5TO56enrhw4QKaNGkCqVSKZcuWYcuWLQCA27dvw8LCAubm5kJxEb6AMqY6uLHG3hdurL0bbqy9G37WYH+Ex7AxtdS2bVt8++23r1wg4+PjYWZmxhdOxhhjjDGmErjBxtTWi42y69evY+PGjYiJicHZs2dhbGwsYmSMMcYYY4zV4AYbU3vl5eW4desWZDIZ4uPjuXQ/Y4wxxhhTGVx0hDHUNNqqqqq47z1jjDHGGFMp3GBjjDHGGGOMMRXFVSIZY4wxxhhjTEVxg40xxhhjjDHGVBQ32BhjjDHGGGNMRXGDjTHGGGOMMcZUFDfYGGOMMcYYY0xFcYONMcYYY4wxxlQUN9gYY4wxxhhjTEVxg40xxhhjjDHGVBQ32BhjjH0wHj9+jDFjxqBJkyaQSqVo0KABunXrhsTERLFDe8X27dshkUggkUigoaEBc3NzDB48GLm5uWKHxhhjTIVoiR0AY4wx9lcZMGAAKioqsGPHDjRt2hSPHj3CqVOnUFBQ8Lf9zoqKCujo6LzTvsbGxsjIyAAR4c6dOxg7diyCgoLw008//cVRMsYY+6fiN2yMMcY+CIWFhYiPj8fSpUvRuXNnWFpaol27dpgxYwZ69+6ttN3o0aNhZmYGXV1dODg44PDhw8L6vXv3olWrVpBKpbCyssIXX3yh9HusrKywcOFChISEwNjYGGFhYQCAhIQEeHl5QU9PD40bN0ZERASePXv2hzFLJBI0aNAA5ubm8PDwQGhoKC5evIiioiJhm8jISLRo0QL6+vpo2rQpZs+ejcrKSmH9vHnz4OzsjOjoaFhZWcHExATBwcEoLi4WtikuLsawYcNgYGAAc3NzrFq1Cp06dcKECROEbcrLyzFlyhQ0atQIBgYGaN++PWJjY9/q/wFjjLG/HjfYGGOMfRAMDQ1haGiIAwcOoLy8/LXbVFdXo0ePHkhMTERMTAzS09OxZMkSaGpqAgCuXLmCQYMGITg4GNevX8e8efMwe/ZsbN++XennrFixAk5OTrh27Rpmz56N7OxsdO/eHQMGDEBKSgp2796NhIQEhIeHv3H8+fn52L9/PzQ1NYV4AMDIyAjbt29Heno61qxZgy1btmDVqlVK+2ZnZ+PAgQM4fPgwDh8+jLi4OCxZskRYP2nSJCQmJuLgwYM4efIk4uPjcfXqVaWfER4ejvPnz2PXrl1ISUlBUFAQunfvjqysrDf+GxhjjP0NiDHGGPtAfPfdd2Rqakq6urrk4eFBM2bMoOTkZGH98ePHSUNDgzIyMl67/9ChQ8nf319p2dSpU8ne3l74bGlpSX379lXaJjQ0lMLCwpSWxcfHk4aGBj1//vy1v2vbtm0EgAwMDEhfX58AEACKiIj4w79x+fLl1KZNG+Hz3LlzSV9fn4qKipRibt++PRERFRUVkba2Nu3Zs0dYX1hYSPr6+vTpp58SEVFOTg5pamrSgwcPlH6Xr68vzZgx4w/jYYwx9vfiN2yMMcY+GAMGDMDDhw9x8OBBdO/eHbGxsXB1dRXekCUlJcHCwgItWrR47f43btyAp6en0jJPT09kZWVBLpcLy9zc3JS2SU5Oxvbt24W3fIaGhujWrRuqq6tx586d343XyMgISUlJuHz5Mr744gu4urpi0aJFStvs3r0bnp6eaNCgAQwNDTFr1qxXCpNYWVnByMhI+Gxubo78/HwAwO3bt1FZWYl27doJ601MTNCyZUvh8/Xr1yGXy9GiRQulvyEuLg7Z2dm/Gz9jjLG/HxcdYYwx9kHR1dWFv78//P39MXv2bHz88ceYO3cuRo4cCT09vb/kdxgYGCh9LikpwejRoxEREfHKtk2aNPndn6OhoYHmzZsDAOzs7JCdnY0xY8YgOjoaAHD+/HkMGzYM8+fPR7du3WBiYoJdu3a9Mq5OW1tb6bNEIkF1dfUb/z0lJSXQ1NTElStXlLpjAjVdTRljjImHG2yMMcY+aPb29jhw4AAAoHXr1rh//z4yMzNf+5bNzs7ulSkAEhMT0aJFi1caMi9ydXVFenq60Ph6V9OnT0ezZs0wceJEuLq64ty5c7C0tMTMmTOFbXJyct7qZzZt2hTa2tq4dOmS0Hh8+vQpMjMz4e3tDQBwcXGBXC5Hfn4+vLy8/qe/gTHG2F+Lu0Qyxhj7IBQUFKBLly6IiYlBSkoK7ty5gz179mDZsmXo06cPAMDHxwfe3t4YMGAATp48iTt37uDo0aM4duwYAGDy5Mk4deoUFi5ciMzMTOzYsQPr1q3DlClT/vB3R0ZG4ty5cwgPD0dSUhKysrLw/fffv1XREQBo3Lgx+vXrhzlz5gAAbGxskJubi127diE7Oxtffvkl9u/f/1Y/08jICCNGjMDUqVNx5swZpKWlITQ0FBoaGpBIJACAFi1aYNiwYQgJCcG+fftw584dXLx4EYsXL8YPP/zwVr+PMcbYX4sbbIwxxj4IhoaGaN++PVatWgVvb284ODhg9uzZGDVqFNatWydst3fvXrRt2xZDhgyBvb09pk2bJoxPc3V1xf/93/9h165dcHBwwJw5c7BgwQKMHDnyD39369atERcXh8zMTHh5ecHFxQVz5sxBw4YN3/rvmDhxIn744QdcvHgRvXv3xsSJExEeHg5nZ2ecO3cOs2fPfuufuXLlSri7uyMwMBB+fn7w9PSEnZ0ddHV1hW22bduGkJAQTJ48GS1btkTfvn2V3soxxhgTh4SISOwgGGOMMfb+PHv2DI0aNcIXX3yB0NBQscNhjDH2B3gMG2OMMfaBu3btGm7evIl27drh6dOnWLBgAQAIXUUZY4ypLm6wMcYYY2pgxYoVyMjIgI6ODtq0aYP4+HjUrVtX7LAYY4z9Ce4SyRhjjDHGGGMqiouOMMYYY4wxxpiK4gYbY4wxxhhjjKkobrAxxhhjjDHGmIriBhtjjDHGGGOMqShusDHGGGOMMcaYiuIGG2OMMcYYY4ypKG6wMcYYY4wxxpiK4gYbY4wxxhhjjKkobrAxxhhjjDHGmIr6f1bkvlkGSpeJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算每个组的占比\n",
    "group_percentage = group_counts / group_counts.sum() * 100\n",
    "\n",
    "# 生成柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = group_counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
    "\n",
    "# 在每个柱子上显示数量和占比\n",
    "for i, count in enumerate(group_counts):\n",
    "    percentage = group_percentage[i]\n",
    "    ax.text(i, count + 0.1, f'{count} ({percentage:.1f}%)', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.title('Score Distribution')\n",
    "plt.xlabel('Score Range')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)  # 旋转x轴标签以便清晰显示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cffd1841-7b44-4105-9c56-e289fb4b9b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rapidfuzz import fuzz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e6454fa-e698-4ade-89eb-32de0c47750a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.ratio(\"Cocu S.\",\"Çöcü, S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f78d7a2d-abb5-4a34-b380-bbf37a51ca5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.285714285714292"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.token_ratio(\"Cocu S.\",\"Çöcü, S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d5ace6f5-6b95-460f-97fd-2ab8a82141c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_ratio(\"Cocu S.\",\"Çöcü, S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "747d3014-6591-4a3a-8379-ffe8737a2b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_token_ratio(\"Cocu S.\",\"Çöcü, S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4614e97f-eb62-40f7-9915-09786db96d6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col_name other_col\n",
      "0         4         a\n",
      "1         5         b\n",
      "2         2         c\n",
      "3         1         d\n",
      "4         3         e\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e317511-0779-4178-8cf7-389ac8940e31",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
